Botany 2020
Setup
Install
Prior to starting this workshop, you should have had the following packages installed.
## Make a list of packages
list_of_packages <- c("tidyverse", "ridigbio", "spocc",
"scrubr", "RCurl", "rjson",
"sp", "raster", "maptools",
"maps", "rgdal", "mapproj",
"ggplot2", "ENMeval", "dismo",
"RStoolbox", "hypervolume", "gtools",
"caret", "phytools", "factoextra",
"FactoMiner", "alphanull", "picante",
"devtools")
## If you do not have these packahes installed, install the package
new.packages <- list_of_packages[!(list_of_packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
## Make sure all packages load
lapply(list_of_packages, require, character.only = TRUE)
# Install packages directly from github
library(devtools)
## ggbiplot
install_github("vqv/ggbiplot")
### If you have R v.3.6
#install_github("vqv/ggbiplot", ref = "experimental")
### If you are having issues installing ggbiplot, you may have to uninstall the package 'backports' and reset R.
## ENMTools
library(devtools)
install_github("danlwarren/ENMTools")
library(ENMTools)
# If ENMTools does not install
# library(devtools)
# install_github(repo = "ecospat/ecospat/ecospat", ref="3.1")
# library(ecospat)
# install_github("danlwarren/ENMTools")
## If you are still having issues, you may need to reset R and try again.
## ENMeval
library(devtools)
install_github("bobmuscarella/ENMeval@master", force = TRUE)
## rSDM
devtools::install_github("Pakillo/rSDM", force = TRUE)
## spThin
library(devtools)
install_github('johnbaums/rmaxent',"spThin")What are these packages?
tidyverse is a collection of R packages that are used for “everyday data analysis” including ggplot2, dplyr, tidyr, readr, purr, tibble, stringr, forcats. We use dplyr and ggplot2 throughout the many R scripts.
ridigbio is an R package that query the iDigBio API.
spocc query data from GBIF, USGS’ Biodiversity Information Serving Our Nation (BISON), iNaturalist, Berkeley Ecoinformatics Engine, eBird, iDigBio, VertNet, Ocean Biogeographic Information System (OBIS), and Atlas of Living Australia (ALA).
scrubr can be used to clean the data.
RCurl and rjson are used to query an application programming interface (API), or an online database.
Geospatial packages include sp, raster, maptools, maps, rgdal, RStoolbox, and mapproj.
ggplot2 is used to plot and visualize data. ggbiplot is an add on to ggplot2.
ENMTools, ENMeval, and dismo are specific to ecological niche modeling.
hypervolume and caret have functions related to statistics.
phytools and picante are related to phylogenetics.
factoextra and FactoMiner are used for statistics related to a principal components analysis.
Troubleshooting
This exercise required the package ENMTools. This package requires Java (and the Oracle Java JDK) and that the maxent.jar file is in the folder that contains the dismo package (this package is a dependency of ENMTools). To find out where to put the jar file, run the command
system.file("java", package="dismo")Once you have your java and maxent file set, you are ready to go! If you have more issues, check out: https://github.com/MTFA/CohortEx/wiki/Run-rJava-with-RStudio-under-OSX-10.10,-10.11-(El-Capitan)-or-10.12-(Sierra)
To download the jar file to the right directory —
library(devtools)
install_github("johnbaums/rmaxent")
library(rmaxent)
get_maxent(version = "latest", quiet = FALSE)Then reload rgdal (it is masked)
If you are having error with rJava
## Remove rJava
remove.packages(rJava)
## Update your R Java configuration
system("sudo R CMD javareconf")
## Restart R and check to see if the path matches
Sys.getenv("DYLD_FALLBACK_LIBRARY_PATH")
## Reinstall rJava
install.packages("rJava")
library(rJava)If you are still having issues with rJava - check to see if you are using Java 12 (this should be included in the error message)
system("java -version")The inital relase of java 12 does not work - install an old version instead.
Install an old version instead Find on the Oracle website or use homebrew.
system("brew cask install homebrew/cask-versions/java11")Restart R and redo the previous steps. Required: unistall the newer versions of java prior to repeating and restart R after.
## Uninstall Java 12, then repeat the following
## Remove rJava
remove.packages(rJava)
## Update your R Java configuration
system("sudo R CMD javareconf")
## Restart R and check to see if the path matches
Sys.getenv("DYLD_FALLBACK_LIBRARY_PATH")
## Reinstall rJava
install.packages("rJava")
library(rJava)If you have additional issues, let us know!
Download Occurrence Data
Original script by AE Melton.
Modified and created by ML Gaynor.
Load Packages
library(ridigbio)
library(spocc)
library(scrubr)Data download from iDigBio
First, we are searching for the species Liatris cylindracea. Next, download occurrence records for the family Liatris belongs too, Asteraceae.
iDigBio_LC <- idig_search_records(rq=list(scientificname="Liatris cylindracea"))
iDigBio_LC_family <- idig_search_records(rq=list(family="Asteraceae"), limit=1000)What if you want to read in all the points for a family within an extent?
Hint: Use the iDigBio portal to determine the bounding box for your region of interest.
First, set up your search input. Check out the ridigbio manual to understand the input parameters.
rq_input <- list("scientificname"=list("type"="exists"),
"family"="asteraceae",
geopoint=list(
type="geo_bounding_box",
top_left=list(lon=-87.86, lat=30.56),
bottom_right=list(lon=-79.21, lat= 24.78)
)
)iDigBio_LC_family_florida <- idig_search_records(rq_input, limit=1000)| uuid | occurrenceid | catalognumber | family | genus | scientificname | country | stateprovince | geopoint.lon | geopoint.lat | datecollected | collector | recordset |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 006b407f-aafc-49e5-82c9-31ff8aff3fe3 | b70c7beb-8a13-4779-8a5d-9e952edc75e9 | brit287702 | asteraceae | liatris | liatris cylindracea | united states | NA | NA | NA | NA | NA | 72d4c3c7-3413-4588-a803-e1a63e0d7c6c |
| 00cb52c6-3606-47ec-94cd-72222ac77f2c | 46d67299-9122-4bb8-80af-1ac000ff76d4 | v0033039wis | asteraceae | liatris | liatris cylindracea | united states | wisconsin | NA | NA | 1958-07-20T00:00:00+00:00 | hartley, thomas | 9e5aede6-bee5-4a3d-a255-513771b20035 |
| 01f04596-0721-4556-8831-86a39cbb2c58 | 373bea21-5138-4730-8915-2347f84e5256 | ind-0137239 | asteraceae | liatris | liatris cylindracea | united states | indiana | NA | NA | 1915-08-22T00:00:00+00:00 | chas. c. deam | 0dab1fc7-ca99-456b-9985-76edbac003e0 |
| 021b6c1d-9b22-45e4-9cea-a26821e65910 | urn:uuid:788e76f1-4e00-4ead-b81a-b30179edff26 | 211373 | asteraceae | liatris | liatris cylindracea | united states | minnesota | -92.83619 | 45.42608 | NA | taylor, b. | e2def7e2-1455-4856-9823-6d3738417d24 |
| 0237adc6-dd85-4f87-9e5c-422996272a9f | a48f306d-d338-471d-a2ee-82100933d41b | b99374 | asteraceae | liatris | liatris cylindracea | united states | wisconsin | NA | NA | 1937-09-08T00:00:00+00:00 |
|
c2767dde-1315-4d78-abf9-8e098dd588ab |
| 039f4bde-2ab1-4561-a9af-1c850a42aab9 | urn:uuid:8c635507-6f3d-4936-b642-221666aafb26 | 211375 | asteraceae | liatris | liatris cylindracea | united states | minnesota | -91.31853 | 43.62916 | 1919-08-01T00:00:00+00:00 | rosendahl, c. | e2def7e2-1455-4856-9823-6d3738417d24 |
Data download using spocc
Using the package spocc we download data from iDigBio and GBIF. We specified that coordinates are needed. Notice that the occurrence records for iDigBio and GBIF are stored sperately.
(spocc_LC <- occ(query = "Liatris cylindracea", from = c('gbif','idigbio'), has_coords = T))## Searched: gbif, idigbio
## Occurrences - Found: 579, Returned: 579
## Search type: Scientific
## gbif: Liatris cylindracea (480)
## idigbio: Liatris cylindracea (99)See the spocc manual to set the extent for a search.
Processing spocc download
Synoymns
Sometimes the ‘name’ in the database does not match the ‘scientific name’- this can be due to synoymns or variety names.
## [,1] [,2]
## [1,] "Liatris cylindracea Michx." "Liatris cylindracea Michx."Since this could be an issue when processing the data, we are going to fix the name using the package scurbr.
spocc_LC_namefixed <- fixnames(spocc_LC, how = "query")## Warning: fixnames will be removed in the next version; see scrubr::fix_namesData format
Since the occurrence records for iDigBio and GBIF are stored sperately, next we have to convert the occurrence download into a single data.frame (df) where all query are combined.
spocc_LC_df <- occ2df(spocc_LC_namefixed)| name | longitude | latitude | prov | date | key |
|---|---|---|---|---|---|
| Liatris cylindracea | -90.62685 | 38.26096 | gbif | 2020-03-18 | 2596076340 |
| Liatris cylindracea | -87.16622 | 33.18885 | gbif | 2020-06-14 | 2802751979 |
| Liatris cylindracea | -89.49381 | 42.69785 | gbif | 2019-07-03 | 2283149029 |
| Liatris cylindracea | -87.39522 | 41.62323 | gbif | 2019-07-13 | 2294490090 |
| Liatris cylindracea | -89.77279 | 43.13965 | gbif | 2019-07-19 | 2294629302 |
| Liatris cylindracea | -89.39378 | 43.18260 | gbif | 2019-07-23 | 2294645919 |
| Liatris cylindracea | -79.42030 | 43.75006 | gbif | 2019-07-23 | 2294650703 |
| Liatris cylindracea | -87.89371 | 42.45455 | gbif | 2019-07-25 | 2294679719 |
| Liatris cylindracea | -83.09190 | 45.98512 | gbif | 2019-07-24 | 2311314905 |
| Liatris cylindracea | -88.20168 | 42.06006 | gbif | 2019-07-26 | 2311338809 |
| Liatris cylindracea | -84.49025 | 44.62769 | gbif | 2019-07-25 | 2311387297 |
| Liatris cylindracea | -87.82655 | 42.53831 | gbif | 2019-07-25 | 2311413181 |
| Liatris cylindracea | -87.80137 | 42.45064 | gbif | 2019-07-25 | 2350326766 |
| Liatris cylindracea | -88.31463 | 42.19416 | gbif | 2019-07-07 | 2397483624 |
| Liatris cylindracea | -81.93469 | 46.09624 | gbif | 2019-07-14 | 2447982139 |
| Liatris cylindracea | -80.04160 | 43.44048 | gbif | 2019-07-31 | 2465114768 |
| Liatris cylindracea | -81.81443 | 43.21843 | gbif | 2019-07-29 | 2540734101 |
| Liatris cylindracea | -82.94771 | 45.92069 | gbif | 2019-08-01 | 2331855805 |
| Liatris cylindracea | -87.80560 | 42.40589 | gbif | 2019-08-02 | 2331867098 |
| Liatris cylindracea | -80.20854 | 42.85761 | gbif | 2019-08-03 | 2331914267 |
| Liatris cylindracea | -82.77755 | 45.69730 | gbif | 2019-08-04 | 2331944192 |
| Liatris cylindracea | -86.03655 | 42.60887 | gbif | 2019-08-04 | 2350326417 |
| Liatris cylindracea | -81.82243 | 42.39630 | gbif | 2019-08-05 | 2350326885 |
| Liatris cylindracea | -87.39025 | 41.61772 | gbif | 2019-08-03 | 2350327056 |
| Liatris cylindracea | -87.39561 | 41.62389 | gbif | 2019-08-03 | 2350327166 |
| Liatris cylindracea | -87.54515 | 41.75903 | gbif | 2019-08-06 | 2350328405 |
| Liatris cylindracea | -82.69163 | 45.61977 | gbif | 2019-08-08 | 2350331158 |
| Liatris cylindracea | -89.43021 | 43.04116 | gbif | 2019-08-09 | 2350338061 |
| Liatris cylindracea | -87.27676 | 41.61042 | gbif | 2019-08-10 | 2350353575 |
| Liatris cylindracea | -81.94163 | 43.21775 | gbif | 2019-08-10 | 2350354522 |
| Liatris cylindracea | -81.97216 | 43.27360 | gbif | 2019-08-10 | 2350354713 |
| Liatris cylindracea | -81.92199 | 43.37881 | gbif | 2019-08-10 | 2350355970 |
| Liatris cylindracea | -86.29775 | 35.78874 | gbif | 2019-08-08 | 2350356515 |
| Liatris cylindracea | -81.87154 | 43.21321 | gbif | 2019-08-09 | 2350359030 |
| Liatris cylindracea | -88.25393 | 42.01357 | gbif | 2019-08-10 | 2350361500 |
| Liatris cylindracea | -81.90357 | 43.29497 | gbif | 2019-08-10 | 2350373293 |
| Liatris cylindracea | -88.20179 | 42.05989 | gbif | 2019-08-11 | 2350377389 |
| Liatris cylindracea | -88.49212 | 41.83114 | gbif | 2019-08-10 | 2350384931 |
| Liatris cylindracea | -87.55979 | 41.67936 | gbif | 2019-08-12 | 2350389298 |
| Liatris cylindracea | -87.55978 | 41.67948 | gbif | 2019-08-12 | 2350391421 |
| Liatris cylindracea | -81.83265 | 42.27649 | gbif | 2019-08-12 | 2350404366 |
| Liatris cylindracea | -81.97914 | 42.29385 | gbif | 2019-08-14 | 2350423491 |
| Liatris cylindracea | -81.85499 | 42.22794 | gbif | 2019-08-14 | 2350437522 |
| Liatris cylindracea | -82.68709 | 45.66322 | gbif | 2019-08-14 | 2350439840 |
| Liatris cylindracea | -87.90551 | 42.46278 | gbif | 2019-08-03 | 2350444378 |
| Liatris cylindracea | -81.87792 | 45.97071 | gbif | 2019-08-14 | 2350446287 |
| Liatris cylindracea | -90.81668 | 38.46749 | gbif | 2019-08-17 | 2366065927 |
| Liatris cylindracea | -87.80143 | 42.45080 | gbif | 2019-08-17 | 2366094074 |
| Liatris cylindracea | -87.80476 | 42.41891 | gbif | 2019-08-18 | 2366108212 |
| Liatris cylindracea | -87.20401 | 41.62109 | gbif | 2019-08-20 | 2366133525 |
| Liatris cylindracea | -81.98693 | 42.28380 | gbif | 2019-08-14 | 2366142035 |
| Liatris cylindracea | -81.88928 | 42.31200 | gbif | 2019-08-14 | 2366142290 |
| Liatris cylindracea | -89.34788 | 41.87327 | gbif | 2019-08-21 | 2397511006 |
| Liatris cylindracea | -89.20761 | 42.21227 | gbif | 2019-08-22 | 2397511044 |
| Liatris cylindracea | -89.77283 | 43.14011 | gbif | 2019-08-24 | 2397544031 |
| Liatris cylindracea | -81.82863 | 43.38344 | gbif | 2019-08-24 | 2397545195 |
| Liatris cylindracea | -80.17430 | 42.58551 | gbif | 2019-08-23 | 2397546505 |
| Liatris cylindracea | -88.31720 | 42.19544 | gbif | 2019-08-23 | 2397561898 |
| Liatris cylindracea | -88.25443 | 42.01464 | gbif | 2019-08-23 | 2397564596 |
| Liatris cylindracea | -81.92915 | 42.38429 | gbif | 2019-08-26 | 2397581937 |
| Liatris cylindracea | -81.84695 | 42.23364 | gbif | 2019-08-25 | 2397581984 |
| Liatris cylindracea | -81.98480 | 42.29205 | gbif | 2019-08-26 | 2397584078 |
| Liatris cylindracea | -82.69442 | 45.78553 | gbif | 2019-08-27 | 2397595498 |
| Liatris cylindracea | -89.77349 | 43.13907 | gbif | 2019-08-08 | 2397596192 |
| Liatris cylindracea | -91.76531 | 43.80910 | gbif | 2019-08-22 | 2397605137 |
| Liatris cylindracea | -95.10042 | 43.78117 | gbif | 2019-08-28 | 2397610615 |
| Liatris cylindracea | -87.80180 | 42.45135 | gbif | 2019-08-28 | 2397623166 |
| Liatris cylindracea | -89.17146 | 42.13536 | gbif | 2019-08-27 | 2397626885 |
| Liatris cylindracea | -89.62310 | 42.27654 | gbif | 2019-08-29 | 2397662767 |
| Liatris cylindracea | -81.99679 | 43.29185 | gbif | 2019-08-31 | 2397681829 |
| Liatris cylindracea | -82.74137 | 45.78097 | gbif | 2019-08-10 | 2422875270 |
| Liatris cylindracea | -89.44227 | 43.23465 | gbif | 2019-08-27 | 2422995129 |
| Liatris cylindracea | -86.03548 | 42.60680 | gbif | 2019-08-15 | 2423013181 |
| Liatris cylindracea | -81.90362 | 42.32986 | gbif | 2019-08-14 | 2429230412 |
| Liatris cylindracea | -81.80529 | 42.29334 | gbif | 2019-08-13 | 2445011465 |
| Liatris cylindracea | -81.99733 | 42.22599 | gbif | 2019-08-17 | 2445121474 |
| Liatris cylindracea | -91.42895 | 43.77074 | gbif | 2019-08-22 | 2445137307 |
| Liatris cylindracea | -80.07392 | 43.52519 | gbif | 2019-08-07 | 2465116216 |
| Liatris cylindracea | -88.20208 | 42.06030 | gbif | 2019-08-23 | 2465120000 |
| Liatris cylindracea | -81.83886 | 42.24473 | gbif | 2019-08-04 | 2517952699 |
| Liatris cylindracea | -80.32013 | 43.46092 | gbif | 2019-08-13 | 2517954637 |
| Liatris cylindracea | -81.97693 | 43.28063 | gbif | 2019-08-29 | 2517954794 |
| Liatris cylindracea | -81.64483 | 46.37250 | gbif | 2019-08-22 | 2540737415 |
| Liatris cylindracea | -89.20591 | 42.21140 | gbif | 2019-08-16 | 2576419875 |
| Liatris cylindracea | -89.39355 | 43.18280 | gbif | 2019-08-14 | 2580229150 |
| Liatris cylindracea | -89.30190 | 48.58295 | gbif | 2019-08-18 | 2813977735 |
| Liatris cylindracea | -81.89001 | 42.33353 | gbif | 2019-09-04 | 2422925996 |
| Liatris cylindracea | -88.27338 | 42.15473 | gbif | 2019-09-08 | 2422978756 |
| Liatris cylindracea | -89.05805 | 42.98933 | gbif | 2019-09-05 | 2422979604 |
| Liatris cylindracea | -82.99418 | 45.80779 | gbif | 2019-09-06 | 2422979686 |
| Liatris cylindracea | -83.01787 | 45.91057 | gbif | 2019-09-12 | 2423048695 |
| Liatris cylindracea | -81.80096 | 42.36401 | gbif | 2019-09-13 | 2423057406 |
| Liatris cylindracea | -82.76924 | 45.99909 | gbif | 2019-09-12 | 2423063072 |
| Liatris cylindracea | -91.23133 | 37.18044 | gbif | 2019-09-10 | 2423064456 |
| Liatris cylindracea | -90.77232 | 43.42788 | gbif | 2019-09-14 | 2423075440 |
| Liatris cylindracea | -90.77202 | 43.42764 | gbif | 2019-09-14 | 2423078113 |
| Liatris cylindracea | -82.70426 | 45.94042 | gbif | 2019-09-12 | 2423083061 |
| Liatris cylindracea | -83.07334 | 43.99958 | gbif | 2019-09-15 | 2423089176 |
| Liatris cylindracea | -90.19143 | 38.15793 | gbif | 2019-09-22 | 2423247267 |
| Liatris cylindracea | -81.90281 | 42.32196 | gbif | 2019-09-29 | 2429308076 |
| Liatris cylindracea | -91.01993 | 42.73446 | gbif | 2019-09-16 | 2445035832 |
| Liatris cylindracea | -81.86831 | 43.37468 | gbif | 2019-09-02 | 2517955229 |
| Liatris cylindracea | -81.88395 | 42.24337 | gbif | 2019-09-10 | 2517956003 |
| Liatris cylindracea | -88.27204 | 41.92578 | gbif | 2019-09-12 | 2540738310 |
| Liatris cylindracea | -90.18969 | 38.15720 | gbif | 2019-09-03 | 2542904732 |
| Liatris cylindracea | -90.62695 | 38.25671 | gbif | 2019-09-23 | 2573822203 |
| Liatris cylindracea | -88.18185 | 41.89795 | gbif | 2019-09-19 | 2576312278 |
| Liatris cylindracea | -81.84571 | 42.27222 | gbif | 2019-10-11 | 2429553598 |
| Liatris cylindracea | -91.22471 | 36.31743 | gbif | 2019-11-04 | 2451726900 |
| Liatris cylindracea | -87.54422 | 41.75960 | gbif | 2018-04-28 | 1847423249 |
| Liatris cylindracea | -89.49863 | 42.68726 | gbif | 2018-07-18 | 1883562246 |
| Liatris cylindracea | -81.88823 | 43.22347 | gbif | 2018-07-19 | 1883563104 |
| Liatris cylindracea | -91.41243 | 43.95158 | gbif | 2018-07-21 | 1883931517 |
| Liatris cylindracea | -81.90000 | 43.30244 | gbif | 2018-07-19 | 1883938526 |
| Liatris cylindracea | -91.40139 | 43.95866 | gbif | 2018-07-22 | 1883943054 |
| Liatris cylindracea | -83.75256 | 44.53482 | gbif | 2018-07-05 | 1883963011 |
| Liatris cylindracea | -91.01829 | 42.73412 | gbif | 2018-07-23 | 1890039481 |
| Liatris cylindracea | -80.09582 | 43.57053 | gbif | 2018-07-28 | 1890054025 |
| Liatris cylindracea | -88.25598 | 42.01284 | gbif | 2018-07-29 | 1890059243 |
| Liatris cylindracea | -87.57331 | 41.78203 | gbif | 2018-07-24 | 1890059904 |
| Liatris cylindracea | -87.54423 | 41.75961 | gbif | 2018-07-29 | 1890064325 |
| Liatris cylindracea | -80.08350 | 43.58716 | gbif | 2018-07-30 | 1890071904 |
| Liatris cylindracea | -81.83987 | 43.20576 | gbif | 2018-07-30 | 1890074211 |
| Liatris cylindracea | -79.55828 | 43.69791 | gbif | 2018-07-30 | 1890074974 |
| Liatris cylindracea | -83.60155 | 42.23280 | gbif | 2018-07-29 | 1890674100 |
| Liatris cylindracea | -90.75028 | 44.29741 | gbif | 2018-07-30 | 1890707606 |
| Liatris cylindracea | -90.75057 | 44.30033 | gbif | 2018-07-30 | 1890724619 |
| Liatris cylindracea | -88.25505 | 42.01478 | gbif | 2018-07-31 | 1891295584 |
| Liatris cylindracea | -83.69037 | 42.30337 | gbif | 2018-07-29 | 1899767042 |
| Liatris cylindracea | -91.40949 | 43.95753 | gbif | 2018-07-21 | 1914310744 |
| Liatris cylindracea | -84.49331 | 39.06933 | gbif | 2018-07-25 | 1920787697 |
| Liatris cylindracea | -89.49863 | 42.68726 | gbif | 2018-07-18 | 1990471361 |
| Liatris cylindracea | -80.05726 | 43.47835 | gbif | 2018-07-30 | 2465087376 |
| Liatris cylindracea | -79.53908 | 43.74333 | gbif | 2018-07-26 | 2540717475 |
| Liatris cylindracea | -87.57825 | 41.78325 | gbif | 2018-07-28 | 2626116351 |
| Liatris cylindracea | -80.12584 | 43.52970 | gbif | 2018-07-29 | 2813865345 |
| Liatris cylindracea | -89.33311 | 41.23892 | gbif | 2018-08-03 | 1890680138 |
| Liatris cylindracea | -87.80849 | 42.42209 | gbif | 2018-08-04 | 1890688568 |
| Liatris cylindracea | -81.90017 | 42.38337 | gbif | 2018-08-05 | 1890698827 |
| Liatris cylindracea | -89.05772 | 42.98993 | gbif | 2018-08-05 | 1890710637 |
| Liatris cylindracea | -77.84414 | 44.03936 | gbif | 2018-08-07 | 1890712871 |
| Liatris cylindracea | -89.77215 | 43.13979 | gbif | 2018-08-03 | 1890715967 |
| Liatris cylindracea | -87.87305 | 41.77304 | gbif | 2018-08-11 | 1891310532 |
| Liatris cylindracea | -89.38538 | 41.92383 | gbif | 2018-08-08 | 1891325032 |
| Liatris cylindracea | -81.84592 | 42.30069 | gbif | 2018-08-12 | 1891325190 |
| Liatris cylindracea | -87.57331 | 41.78203 | gbif | 2018-08-11 | 1891327188 |
| Liatris cylindracea | -88.31518 | 42.19575 | gbif | 2018-08-09 | 1891334163 |
| Liatris cylindracea | -88.31226 | 42.14957 | gbif | 2018-08-17 | 1898797804 |
| Liatris cylindracea | -87.80472 | 42.41842 | gbif | 2018-08-17 | 1898800066 |
| Liatris cylindracea | -87.80514 | 42.41889 | gbif | 2018-08-17 | 1898800190 |
| Liatris cylindracea | -87.80269 | 42.45021 | gbif | 2018-08-18 | 1898810701 |
| Liatris cylindracea | -89.05806 | 42.98947 | gbif | 2018-08-18 | 1898834065 |
| Liatris cylindracea | -89.77281 | 43.14006 | gbif | 2018-08-14 | 1898834091 |
| Liatris cylindracea | -92.65693 | 44.52560 | gbif | 2018-08-21 | 1898843056 |
| Liatris cylindracea | -81.98080 | 42.21883 | gbif | 2018-08-14 | 1901034927 |
| Liatris cylindracea | -89.05834 | 42.99005 | gbif | 2018-08-25 | 1901036256 |
| Liatris cylindracea | -87.80143 | 42.45066 | gbif | 2018-08-03 | 1914194648 |
| Liatris cylindracea | -87.78765 | 41.86717 | gbif | 2018-08-14 | 1914195357 |
| Liatris cylindracea | -89.36965 | 41.90080 | gbif | 2018-08-18 | 1914195583 |
| Liatris cylindracea | -87.80263 | 42.45006 | gbif | 2018-08-19 | 1914195758 |
| Liatris cylindracea | -87.57331 | 41.78203 | gbif | 2018-08-18 | 1914196356 |
| Liatris cylindracea | -86.04611 | 42.76894 | gbif | 2018-08-18 | 1914348645 |
| Liatris cylindracea | -91.07192 | 37.09674 | gbif | 2018-08-31 | 1914352037 |
| Liatris cylindracea | -87.80474 | 42.41722 | gbif | 2018-08-22 | 1948753365 |
| Liatris cylindracea | -79.50747 | 43.75722 | gbif | 2018-08-19 | 2005266956 |
| Liatris cylindracea | -91.81193 | 38.14330 | gbif | 2018-08-13 | 2006091296 |
| Liatris cylindracea | -81.88098 | 43.20848 | gbif | 2018-08-19 | 2294401867 |
| Liatris cylindracea | -83.07481 | 43.99593 | gbif | 2018-08-05 | 2397448175 |
| Liatris cylindracea | -82.69326 | 45.73200 | gbif | 2018-08-07 | 2422915938 |
| Liatris cylindracea | -82.66572 | 45.77302 | gbif | 2018-08-07 | 2422916195 |
| Liatris cylindracea | -80.15631 | 43.44793 | gbif | 2018-08-07 | 2465087402 |
| Liatris cylindracea | -83.45101 | 38.88592 | gbif | 2018-08-04 | 2529316034 |
| Liatris cylindracea | -81.88460 | 43.29642 | gbif | 2018-08-04 | 2540717651 |
| Liatris cylindracea | -80.46163 | 42.73060 | gbif | 2018-08-01 | 2557766806 |
| Liatris cylindracea | -89.06015 | 42.99205 | gbif | 2018-09-08 | 1913164558 |
| Liatris cylindracea | -89.05841 | 42.98910 | gbif | 2018-09-08 | 1913165897 |
| Liatris cylindracea | -81.98628 | 42.23625 | gbif | 2018-09-08 | 1913178556 |
| Liatris cylindracea | -88.25508 | 42.01475 | gbif | 2018-09-16 | 1913685389 |
| Liatris cylindracea | -90.54347 | 38.20224 | gbif | 2018-10-01 | 1914345018 |
| Liatris cylindracea | -87.80004 | 42.45454 | gbif | 2018-10-06 | 1920805907 |
| Liatris cylindracea | -80.59191 | 42.70125 | gbif | 2018-10-13 | 1931495094 |
| Liatris cylindracea | -87.74641 | 40.21564 | gbif | 2018-10-14 | 1931503386 |
| Liatris cylindracea | -87.74547 | 40.21462 | gbif | 2018-10-14 | 1931503491 |
| Liatris cylindracea | -87.93044 | 42.57168 | gbif | 2018-10-13 | 1932309128 |
| Liatris cylindracea | -89.74419 | 44.27951 | gbif | 2017-07-09 | 1572386698 |
| Liatris cylindracea | -79.57448 | 43.67619 | gbif | 2017-07-12 | 1586098036 |
| Liatris cylindracea | -88.48202 | 42.87147 | gbif | 2017-07-27 | 1586107794 |
| Liatris cylindracea | -89.05739 | 42.99125 | gbif | 2017-07-14 | 1640041333 |
| Liatris cylindracea | -89.05973 | 42.99013 | gbif | 2017-07-23 | 1640081143 |
| Liatris cylindracea | -89.80954 | 43.10593 | gbif | 2017-07-30 | 1640088544 |
| Liatris cylindracea | -89.39449 | 43.18241 | gbif | 2017-07-30 | 1668883155 |
| Liatris cylindracea | -88.36231 | 42.09077 | gbif | 2017-07-03 | 1836687918 |
| Liatris cylindracea | -89.01868 | 42.32850 | gbif | 2017-08-06 | 1586144059 |
| Liatris cylindracea | -90.51111 | 41.43678 | gbif | 2017-08-07 | 1586148475 |
| Liatris cylindracea | -84.65049 | 44.65616 | gbif | 2017-08-04 | 1586925946 |
| Liatris cylindracea | -87.54650 | 41.75860 | gbif | 2017-08-12 | 1586934321 |
| Liatris cylindracea | -90.91875 | 43.03481 | gbif | 2017-08-12 | 1586936620 |
| Liatris cylindracea | -90.59550 | 43.17681 | gbif | 2017-08-12 | 1586938713 |
| Liatris cylindracea | -90.59428 | 43.17674 | gbif | 2017-08-12 | 1586939642 |
| Liatris cylindracea | -89.05808 | 42.98948 | gbif | 2017-08-14 | 1586947741 |
| Liatris cylindracea | -87.80483 | 42.41762 | gbif | 2017-08-19 | 1621782362 |
| Liatris cylindracea | -89.06039 | 42.99199 | gbif | 2017-08-18 | 1621782908 |
| Liatris cylindracea | -90.80156 | 44.32064 | gbif | 2017-08-20 | 1621801032 |
| Liatris cylindracea | -77.98699 | 44.08402 | gbif | 2017-08-23 | 1621802198 |
| Liatris cylindracea | -77.97258 | 44.18646 | gbif | 2017-08-23 | 1621802202 |
| Liatris cylindracea | -77.84793 | 44.11483 | gbif | 2017-08-23 | 1621802338 |
| Liatris cylindracea | -89.12631 | 40.65682 | gbif | 2017-08-20 | 1640049023 |
| Liatris cylindracea | -89.77192 | 43.13977 | gbif | 2017-08-25 | 1640090810 |
| Liatris cylindracea | -89.51541 | 43.02454 | gbif | 2017-08-06 | 1640092507 |
| Liatris cylindracea | -89.12686 | 40.65733 | gbif | 2017-08-23 | 1640099964 |
| Liatris cylindracea | -90.59409 | 43.17872 | gbif | 2017-08-12 | 1640104348 |
| Liatris cylindracea | -90.92108 | 43.03458 | gbif | 2017-08-12 | 1640119043 |
| Liatris cylindracea | -89.05958 | 42.99066 | gbif | 2017-08-26 | 1640121164 |
| Liatris cylindracea | -89.05909 | 42.99085 | gbif | 2017-08-26 | 1668826441 |
| Liatris cylindracea | -81.98498 | 43.23406 | gbif | 2017-08-28 | 1668843264 |
| Liatris cylindracea | -89.05973 | 42.99013 | gbif | 2017-08-05 | 1677281046 |
| Liatris cylindracea | -87.80461 | 42.41739 | gbif | 2017-08-13 | 1677326118 |
| Liatris cylindracea | -89.18842 | 42.93090 | gbif | 2017-08-05 | 1703207537 |
| Liatris cylindracea | -86.30509 | 35.85191 | gbif | 2017-08-25 | 1914197983 |
| Liatris cylindracea | -77.84368 | 44.14095 | gbif | 2017-08-10 | 1993753945 |
| Liatris cylindracea | -83.41716 | 38.97641 | gbif | 2017-08-19 | 2236221183 |
| Liatris cylindracea | -83.46504 | 38.80499 | gbif | 2017-08-19 | 2236221520 |
| Liatris cylindracea | -89.05756 | 42.99076 | gbif | 2017-08-05 | 2445094349 |
| Liatris cylindracea | -92.16922 | 35.96823 | gbif | 2017-09-03 | 1640089470 |
| Liatris cylindracea | -77.86183 | 44.01423 | gbif | 2017-09-05 | 1640099376 |
| Liatris cylindracea | -89.49340 | 42.69841 | gbif | 2017-09-06 | 1640104962 |
| Liatris cylindracea | -89.71798 | 43.13156 | gbif | 2017-09-08 | 1640120915 |
| Liatris cylindracea | -77.88411 | 44.05421 | gbif | 2017-09-05 | 1699378358 |
| Liatris cylindracea | -89.39243 | 43.18356 | gbif | 2017-09-19 | 1831149516 |
| Liatris cylindracea | -87.57608 | 41.71460 | gbif | 2016-07-26 | 1291154225 |
| Liatris cylindracea | -87.64774 | 41.84284 | gbif | 2016-07-28 | 1291154355 |
| Liatris cylindracea | -89.05757 | 42.99036 | gbif | 2016-07-30 | 1291159304 |
| Liatris cylindracea | -85.71997 | 43.44973 | gbif | 2016-07-23 | 1322997904 |
| Liatris cylindracea | -87.99824 | 42.52563 | gbif | 2016-07-30 | 1453168486 |
| Liatris cylindracea | -87.90689 | 42.48217 | gbif | 2016-07-30 | 1453168650 |
| Liatris cylindracea | -83.36147 | 45.96419 | gbif | 2016-07-24 | 2012952611 |
| Liatris cylindracea | -82.73759 | 45.61861 | gbif | 2016-07-24 | 2012952614 |
| Liatris cylindracea | -77.86305 | 44.07157 | gbif | 2016-07-31 | 2563557365 |
| Liatris cylindracea | -87.82715 | 42.36894 | gbif | 2016-08-06 | 1291670420 |
| Liatris cylindracea | -89.33770 | 41.90610 | gbif | 2016-08-08 | 1291677710 |
| Liatris cylindracea | -89.06095 | 42.99140 | gbif | 2016-08-20 | 1315052190 |
| Liatris cylindracea | -80.06046 | 42.54193 | gbif | 2016-08-11 | 2563549536 |
| Liatris cylindracea | -89.84055 | 40.14877 | gbif | 2016-09-05 | 1306585813 |
| Liatris cylindracea | -95.25413 | 45.44381 | gbif | 2015-07-16 | 1805364817 |
| Liatris cylindracea | -92.06722 | 44.47028 | gbif | 2015-08-10 | 1135214267 |
| Liatris cylindracea | -83.45041 | 38.90135 | gbif | 2015-08-19 | 1143523705 |
| Liatris cylindracea | -77.89793 | 44.06960 | gbif | 2015-08-09 | 1453190881 |
| Liatris cylindracea | -81.86100 | 42.20755 | gbif | 2015-08-22 | 1978431574 |
| Liatris cylindracea | -86.20510 | 44.16520 | gbif | 2015-08-13 | 1989262155 |
| Liatris cylindracea | -82.03146 | 45.55013 | gbif | 2015-08-29 | 1990527406 |
| Liatris cylindracea | -90.62555 | 38.25742 | gbif | 2015-09-23 | 1262228557 |
| Liatris cylindracea | -89.06000 | 42.99222 | gbif | 2015-09-02 | 1455566941 |
| Liatris cylindracea | -86.30920 | 35.84616 | gbif | 2015-09-08 | 1455566952 |
| Liatris cylindracea | -92.12878 | 35.95507 | gbif | 2015-09-07 | 1455566954 |
| Liatris cylindracea | -88.05258 | 35.46717 | gbif | 2015-09-11 | 2609176513 |
| Liatris cylindracea | -89.39301 | 43.18290 | gbif | 2014-03-30 | 1455597299 |
| Liatris cylindracea | -89.39100 | 43.18405 | gbif | 2014-03-30 | 1455597300 |
| Liatris cylindracea | -86.04586 | 42.60770 | gbif | 2014-07-29 | 1988722401 |
| Liatris cylindracea | -90.67639 | 44.19250 | gbif | 2014-08-02 | 1024203052 |
| Liatris cylindracea | -88.20065 | 42.06067 | gbif | 2014-08-15 | 1024209902 |
| Liatris cylindracea | -84.64898 | 44.62078 | gbif | 2014-08-26 | 1024218875 |
| Liatris cylindracea | -90.82306 | 38.46583 | gbif | 2014-08-19 | 1258065292 |
| Liatris cylindracea | -80.11498 | 42.48124 | gbif | 2014-08-26 | 1703218792 |
| Liatris cylindracea | -87.02639 | 33.05893 | gbif | 2014-08-17 | 2005285277 |
| Liatris cylindracea | -82.95888 | 45.98204 | gbif | 2014-09-02 | 2012954819 |
| Liatris cylindracea | -77.81174 | 44.19346 | gbif | 2014-09-05 | 2563557752 |
| Liatris cylindracea | -90.52833 | 37.97622 | gbif | 2013-07-26 | 1257980954 |
| Liatris cylindracea | -83.09683 | 45.93570 | gbif | 2013-07-29 | 1931528898 |
| Liatris cylindracea | -92.10115 | 44.39779 | gbif | 2013-08-23 | 1990528787 |
| Liatris cylindracea | -91.70500 | 36.65830 | gbif | 2013-09-08 | 1257994504 |
| Liatris cylindracea | -80.03283 | 42.56807 | gbif | 2013-09-14 | 2294728102 |
| Liatris cylindracea | -88.00617 | 35.54113 | gbif | 2013-09-10 | 2609336706 |
| Liatris cylindracea | -81.98543 | 42.22754 | gbif | 2013-09-08 | 899942247 |
| Liatris cylindracea | -91.36310 | 37.18640 | gbif | 2013-10-23 | 1258050633 |
| Liatris cylindracea | -92.44970 | 36.69610 | gbif | 2013-10-22 | 1258050652 |
| Liatris cylindracea | -82.71642 | 45.67176 | gbif | 2012-07-14 | 1890712775 |
| Liatris cylindracea | -81.93289 | 43.20522 | gbif | 2012-08-19 | 1802599909 |
| Liatris cylindracea | -88.00613 | 35.54085 | gbif | 2012-08-18 | 2540715851 |
| Liatris cylindracea | -90.74426 | 44.29697 | gbif | 2012-08-04 | 891781755 |
| Liatris cylindracea | -90.54080 | 38.20420 | gbif | 2012-09-24 | 1257883005 |
| Liatris cylindracea | -89.77221 | 43.13984 | gbif | 2011-08-14 | 1455597190 |
| Liatris cylindracea | -80.07017 | 42.55773 | gbif | 2011-08-20 | 1990508689 |
| Liatris cylindracea | -87.39828 | 41.62251 | gbif | 2010-07-17 | 891748737 |
| Liatris cylindracea | -80.19311 | 42.43746 | gbif | 2010-08-02 | 1836586916 |
| Liatris cylindracea | -91.03444 | 38.31917 | gbif | 2009-08-21 | 2268820435 |
| Liatris cylindracea | -82.64870 | 45.74304 | gbif | 2009-08-26 | 2366085536 |
| Liatris cylindracea | -79.51408 | 43.74617 | gbif | 2009-10-20 | 2265807122 |
| Liatris cylindracea | -83.11034 | 45.93447 | gbif | 2007-07-19 | 2005339463 |
| Liatris cylindracea | -87.74690 | 40.21827 | gbif | 2007-07-09 | 2234780444 |
| Liatris cylindracea | -80.05904 | 42.46448 | gbif | 2007-09-08 | 1677316926 |
| Liatris cylindracea | -92.72222 | 36.80083 | gbif | 2006-09-08 | 1260453490 |
| Liatris cylindracea | -90.70861 | 38.67861 | gbif | 2004-08-30 | 1261008488 |
| Liatris cylindracea | -87.80857 | 42.42208 | gbif | 2003-08-17 | 1453373091 |
| Liatris cylindracea | -81.96000 | 43.21000 | gbif | 2002-08-14 | 1456194679 |
| Liatris cylindracea | -81.96000 | 43.21000 | gbif | 2002-08-14 | 1899312387 |
| Liatris cylindracea | -81.96000 | 43.21000 | gbif | 2002-08-14 | 2516828214 |
| Liatris cylindracea | -93.66378 | 45.45102 | gbif | 2001-08-28 | 2265430701 |
| Liatris cylindracea | -90.54083 | 38.20388 | gbif | 2001-09-20 | 1258814089 |
| Liatris cylindracea | -93.72596 | 45.46555 | gbif | 1998-08-14 | 2265349667 |
| Liatris cylindracea | -91.42269 | 43.75640 | gbif | 1998-08-08 | 2265430141 |
| Liatris cylindracea | -91.42269 | 43.75640 | gbif | 1997-08-07 | 2265443698 |
| Liatris cylindracea | -90.50000 | 37.78333 | gbif | 1996-09-23 | 1258330970 |
| Liatris cylindracea | -91.03333 | 37.16667 | gbif | 1995-08-01 | 1261880523 |
| Liatris cylindracea | -92.01953 | 43.76751 | gbif | 1994-07-29 | 2265349375 |
| Liatris cylindracea | -92.08925 | 44.09956 | gbif | 1994-07-26 | 2265350368 |
| Liatris cylindracea | -89.87222 | 40.69361 | gbif | 1994-08-18 | 415908558 |
| Liatris cylindracea | -91.70194 | 36.77805 | gbif | 1993-08-15 | 1260978526 |
| Liatris cylindracea | -90.58333 | 37.96667 | gbif | 1993-08-11 | 1261296712 |
| Liatris cylindracea | -104.96147 | 39.73221 | gbif | 1993-08-17 | 2242400797 |
| Liatris cylindracea | -92.74229 | 44.63663 | gbif | 1993-08-04 | 2265349284 |
| Liatris cylindracea | -92.74229 | 44.63663 | gbif | 1993-08-02 | 2265349389 |
| Liatris cylindracea | -87.10000 | 33.00000 | gbif | 1992-07-19 | 56603321 |
| Liatris cylindracea | -90.67444 | 38.12500 | gbif | 1992-08-15 | 2268932005 |
| Liatris cylindracea | -90.67444 | 38.12500 | gbif | 1992-08-15 | 2464647947 |
| Liatris cylindracea | -91.11611 | 38.18805 | gbif | 1992-09-24 | 1261002493 |
| Liatris cylindracea | -84.55462 | 44.74868 | gbif | 1992-09-02 | 1988989334 |
| Liatris cylindracea | -84.57184 | 44.69566 | gbif | 1992-09-01 | 1989228152 |
| Liatris cylindracea | -90.67500 | 38.12500 | gbif | 1992-10-05 | 2268929729 |
| Liatris cylindracea | -86.03300 | 42.60900 | gbif | 1991-08-08 | 233099622 |
| Liatris cylindracea | -90.70000 | 38.16666 | gbif | 1990-08-28 | 1261312058 |
| Liatris cylindracea | -90.62055 | 38.25888 | gbif | 1990-09-02 | 1261003426 |
| Liatris cylindracea | -93.19351 | 45.39232 | gbif | 1989-08-15 | 2265349359 |
| Liatris cylindracea | -92.54074 | 44.49194 | gbif | 1988-06-15 | 2265349858 |
| Liatris cylindracea | -93.53888 | 36.51083 | gbif | 1988-07-25 | 1260965018 |
| Liatris cylindracea | -92.81944 | 44.80028 | gbif | 1988-07-26 | 2265349442 |
| Liatris cylindracea | -84.54181 | 44.45902 | gbif | 1988-08-22 | 1989020896 |
| Liatris cylindracea | -92.91666 | 36.68333 | gbif | 1987-09-19 | 1261002967 |
| Liatris cylindracea | -92.79092 | 44.84076 | gbif | 1987-09-30 | 2265349996 |
| Liatris cylindracea | -92.10861 | 38.41472 | gbif | 1986-07-18 | 1260964907 |
| Liatris cylindracea | -91.99041 | 44.17096 | gbif | 1986-07-31 | 2265349957 |
| Liatris cylindracea | -92.54583 | 44.52361 | gbif | 1986-08-11 | 2265349388 |
| Liatris cylindracea | -92.77505 | 44.85476 | gbif | 1985-06-02 | 2265349779 |
| Liatris cylindracea | -92.54166 | 38.11666 | gbif | 1985-07-25 | 1261007084 |
| Liatris cylindracea | -80.83330 | 40.00000 | gbif | 1985-08-12 | 1228196013 |
| Liatris cylindracea | -83.43154 | 44.44687 | gbif | 1985-08-01 | 1988824682 |
| Liatris cylindracea | -87.00445 | 33.00889 | gbif | 1985-08-01 | 56603146 |
| Liatris cylindracea | -95.24502 | 45.44867 | gbif | 1982-07-29 | 2265349246 |
| Liatris cylindracea | -92.52110 | 44.56198 | gbif | 1982-07-31 | 2265349484 |
| Liatris cylindracea | -83.51282 | 44.54746 | gbif | 1982-08-01 | 1989037167 |
| Liatris cylindracea | -83.41114 | 44.44674 | gbif | 1982-08-07 | 1989165286 |
| Liatris cylindracea | -92.04933 | 44.04203 | gbif | 1982-08-16 | 2265349631 |
| Liatris cylindracea | -82.75000 | 46.78333 | gbif | 1981-08-19 | 1804548575 |
| Liatris cylindracea | -81.86667 | 46.00000 | gbif | 1981-08-15 | 1804560873 |
| Liatris cylindracea | -91.99041 | 44.17096 | gbif | 1980-08-15 | 2265350478 |
| Liatris cylindracea | -96.44879 | 47.73852 | gbif | 1979-08-15 | 2265350144 |
| Liatris cylindracea | -91.42281 | 43.76774 | gbif | 1978-08-14 | 2265349218 |
| Liatris cylindracea | -91.42233 | 43.62329 | gbif | 1978-08-15 | 2265349369 |
| Liatris cylindracea | -81.83333 | 43.26667 | gbif | 1978-09-01 | 1804428271 |
| Liatris cylindracea | -81.83333 | 43.26667 | gbif | 1978-09-01 | 1804574090 |
| Liatris cylindracea | -81.83333 | 43.26667 | gbif | 1978-09-01 | 1804600129 |
| Liatris cylindracea | -92.88527 | 36.69166 | gbif | 1977-07-15 | 1260946785 |
| Liatris cylindracea | -84.65981 | 45.36937 | gbif | 1977-07-12 | 1988815636 |
| Liatris cylindracea | -82.82940 | 45.80870 | gbif | 1976-07-16 | 1839334600 |
| Liatris cylindracea | -80.83492 | 45.27013 | gbif | 1976-08-05 | 1839334611 |
| Liatris cylindracea | -81.86621 | 42.28171 | gbif | 1976-09-05 | 1839334612 |
| Liatris cylindracea | -93.13361 | 35.27833 | gbif | 1972-10-14 | 924820107 |
| Liatris cylindracea | -93.13361 | 35.27833 | gbif | 1972-10-14 | 924820112 |
| Liatris cylindracea | -85.94579 | 42.12044 | gbif | 1970-08-29 | 1989329755 |
| Liatris cylindracea | -90.67583 | 38.50361 | gbif | 1969-08-01 | 1260932129 |
| Liatris cylindracea | -79.42300 | 43.85700 | gbif | 1968-08-25 | 2250594823 |
| Liatris cylindracea | -79.42000 | 43.86000 | gbif | 1968-08-25 | 2304990669 |
| Liatris cylindracea | -79.42000 | 43.86000 | gbif | 1968-08-25 | 2309958506 |
| Liatris cylindracea | -84.36783 | 42.05598 | gbif | 1967-08-15 | 1988659685 |
| Liatris cylindracea | -91.36237 | 43.62304 | gbif | 1966-07-29 | 2265350030 |
| Liatris cylindracea | -87.59237 | 41.56148 | gbif | 1963-08-15 | 2242401085 |
| Liatris cylindracea | -91.55129 | 43.63067 | gbif | 1963-08-21 | 2265349140 |
| Liatris cylindracea | -80.60000 | 42.90000 | gbif | 1963-09-19 | 2250594358 |
| Liatris cylindracea | -80.60000 | 42.90000 | gbif | 1963-09-19 | 2305014115 |
| Liatris cylindracea | -80.60000 | 42.90000 | gbif | 1963-09-19 | 2309823689 |
| Liatris cylindracea | -81.54317 | 44.34863 | gbif | 1962-07-30 | 1638340768 |
| Liatris cylindracea | -81.54317 | 44.34863 | gbif | 1962-07-30 | 1839334604 |
| Liatris cylindracea | -92.49944 | 36.92528 | gbif | 1961-08-02 | 1260987544 |
| Liatris cylindracea | -92.52110 | 44.56198 | gbif | 1960-08-21 | 2265350298 |
| Liatris cylindracea | -82.38690 | 42.99300 | gbif | 1957-06-09 | 2250594555 |
| Liatris cylindracea | -82.39000 | 42.99000 | gbif | 1957-06-09 | 2305853966 |
| Liatris cylindracea | -82.39000 | 42.99000 | gbif | 1957-06-09 | 2308553621 |
| Liatris cylindracea | -83.69666 | 44.44594 | gbif | 1957-08-01 | 1989178809 |
| Liatris cylindracea | -85.80775 | 44.16614 | gbif | 1957-08-18 | 1989380604 |
| Liatris cylindracea | -91.22528 | 39.55194 | gbif | 1956-10-06 | 1260987611 |
| Liatris cylindracea | -85.81486 | 42.13144 | gbif | 1955-09-07 | 1988830248 |
| Liatris cylindracea | -84.31931 | 44.41350 | gbif | 1954-07-12 | 1988708823 |
| Liatris cylindracea | -84.40077 | 44.50095 | gbif | 1953-07-25 | 1989311280 |
| Liatris cylindracea | -84.70668 | 44.63540 | gbif | 1953-07-24 | 1989351547 |
| Liatris cylindracea | -84.12964 | 44.57944 | gbif | 1953-09-27 | 1988687346 |
| Liatris cylindracea | -83.69494 | 44.34345 | gbif | 1952-07-20 | 1989524892 |
| Liatris cylindracea | -84.03907 | 44.57348 | gbif | 1951-07-28 | 1989202098 |
| Liatris cylindracea | -84.28180 | 44.61553 | gbif | 1951-07-31 | 1989288026 |
| Liatris cylindracea | -83.58146 | 44.05117 | gbif | 1951-08-11 | 1988784418 |
| Liatris cylindracea | -83.41105 | 44.42582 | gbif | 1951-08-11 | 1989049524 |
| Liatris cylindracea | -83.20592 | 43.97216 | gbif | 1951-08-09 | 1989177001 |
| Liatris cylindracea | -84.19955 | 42.29950 | gbif | 1951-09-15 | 1989290190 |
| Liatris cylindracea | -86.12800 | 43.35985 | gbif | 1949-07-30 | 1989187265 |
| Liatris cylindracea | -85.62947 | 43.02106 | gbif | 1948-08-17 | 1988599605 |
| Liatris cylindracea | -81.75590 | 43.31246 | gbif | 1945-09-04 | 465928982 |
| Liatris cylindracea | -91.39316 | 43.95302 | gbif | 1943-08-25 | 2265350350 |
| Liatris cylindracea | -79.42000 | 43.70000 | gbif | 1942-08-14 | 1988065648 |
| Liatris cylindracea | -85.88245 | 43.90066 | gbif | 1941-08-03 | 1989207965 |
| Liatris cylindracea | -84.00525 | 42.45669 | gbif | 1941-08-16 | 1989394120 |
| Liatris cylindracea | -85.97790 | 42.54850 | gbif | 1939-07-22 | 1989545608 |
| Liatris cylindracea | -85.97790 | 42.54850 | gbif | 1939-08-07 | 1989365814 |
| Liatris cylindracea | -80.24910 | 42.55910 | gbif | 1938-08-25 | 1839334613 |
| Liatris cylindracea | -92.65777 | 37.08861 | gbif | 1937-07-28 | 1260970503 |
| Liatris cylindracea | -92.21972 | 38.43194 | gbif | 1937-08-18 | 1260971298 |
| Liatris cylindracea | -91.99472 | 37.91638 | gbif | 1937-08-26 | 1260971511 |
| Liatris cylindracea | -92.17861 | 37.89166 | gbif | 1937-08-26 | 1260971530 |
| Liatris cylindracea | -91.79694 | 37.74083 | gbif | 1937-08-27 | 1260971557 |
| Liatris cylindracea | -91.00083 | 39.21722 | gbif | 1937-09-08 | 1260971843 |
| Liatris cylindracea | -91.91861 | 38.80444 | gbif | 1937-09-10 | 1260971923 |
| Liatris cylindracea | -91.31256 | 43.54390 | gbif | 1936-08-20 | 2265349387 |
| Liatris cylindracea | -79.46667 | 43.63333 | gbif | 1934-08-20 | 1899286238 |
| Liatris cylindracea | -79.52797 | 43.66711 | gbif | 1934-08-20 | 465928980 |
| Liatris cylindracea | -82.08000 | 42.75000 | gbif | 1934-09-02 | 1988141918 |
| Liatris cylindracea | -81.50000 | 43.67000 | gbif | 1934-09-03 | 1988211485 |
| Liatris cylindracea | -90.44777 | 38.56833 | gbif | 1931-08-28 | 1260931882 |
| Liatris cylindracea | -79.48333 | 43.63333 | gbif | 1929-09-15 | 1899285105 |
| Liatris cylindracea | -91.97750 | 37.92611 | gbif | 1928-09-02 | 2268815320 |
| Liatris cylindracea | -91.97750 | 37.92611 | gbif | 1927-09-09 | 2268815131 |
| Liatris cylindracea | -83.13098 | 42.67714 | gbif | 1925-08-26 | 1988722234 |
| Liatris cylindracea | -83.72684 | 42.29218 | gbif | 1923-09-01 | 1989085003 |
| Liatris cylindracea | -91.57530 | 36.98690 | gbif | 1922-08-04 | 2268816553 |
| Liatris cylindracea | -79.46667 | 43.63333 | gbif | 1920-08-07 | 1899369426 |
| Liatris cylindracea | -84.61130 | 45.37999 | gbif | 1920-08-21 | 1989176004 |
| Liatris cylindracea | -91.57528 | 36.98694 | gbif | 1920-10-05 | 1260957420 |
| Liatris cylindracea | -91.31853 | 43.62916 | gbif | 1919-08-01 | 2265351238 |
| Liatris cylindracea | -83.61299 | 42.24115 | gbif | 1917-09-03 | 1989001083 |
| Liatris cylindracea | -91.49750 | 40.04277 | gbif | 1915-09-06 | 1260939070 |
| Liatris cylindracea | -91.97750 | 37.92611 | gbif | 1913-10-16 | 2268815553 |
| Liatris cylindracea | -91.57530 | 36.98690 | gbif | 1910-08-05 | 2268813661 |
| Liatris cylindracea | -93.16646 | 44.90516 | gbif | 1909-08-07 | 2265349893 |
| Liatris cylindracea | -91.57530 | 36.98690 | gbif | 1908-09-10 | 2268814511 |
| Liatris cylindracea | -91.57530 | 36.98690 | gbif | 1905-10-08 | 2268813695 |
| Liatris cylindracea | -91.28287 | 43.50806 | gbif | 1899-07-29 | 2265350102 |
| Liatris cylindracea | -96.33441 | 30.62798 | gbif | 1897-01-01 | 1930308135 |
| Liatris cylindracea | -93.75416 | 36.54805 | gbif | 1896-09-28 | 1260926595 |
| Liatris cylindracea | -90.67583 | 38.50361 | gbif | 1896-09-10 | 2268814493 |
| Liatris cylindracea | -91.15305 | 41.46611 | gbif | 1894-08-18 | 415908555 |
| Liatris cylindracea | -83.72633 | 42.27087 | gbif | 1892-08-12 | 1989425627 |
| Liatris cylindracea | -92.26684 | 44.44941 | gbif | 1892-08-20 | 2265350910 |
| Liatris cylindracea | -91.63931 | 44.04996 | gbif | 1888-09-13 | 2265350426 |
| Liatris cylindracea | -92.36995 | 44.41148 | gbif | 1883-08-14 | 2265350636 |
| Liatris cylindracea | -90.67583 | 38.50361 | gbif | 1883-08-25 | 2268815378 |
| Liatris cylindracea | -84.83763 | 43.09254 | gbif | 1880-01-01 | 1988606890 |
| Liatris cylindracea | -88.99055 | 40.51416 | gbif | 1879-08-30 | 415908557 |
| Liatris cylindracea | -92.98853 | 42.11249 | gbif | 1878-08-01 | 1928015915 |
| Liatris cylindracea | -84.83763 | 43.09254 | gbif | 1878-08-01 | 1989045372 |
| Liatris cylindracea | -93.26384 | 44.97996 | gbif | 1878-08-14 | 2265350145 |
| Liatris cylindracea | -90.29208 | 38.64010 | gbif | 1875-08-30 | 1260941240 |
| Liatris cylindracea | -83.30227 | 42.51821 | gbif | 1874-01-01 | 1988958465 |
| Liatris cylindracea | -83.72633 | 42.27087 | gbif | 1861-07-31 | 1988611173 |
| Liatris cylindracea | -83.72633 | 42.27087 | gbif | 1861-08-23 | 1989097124 |
| Liatris cylindracea | -79.00796 | 43.09099 | gbif | 1852-08-16 | 1928848145 |
| Liatris cylindracea | -91.05139 | 39.44889 | gbif | 1850-01-01 | 1258836178 |
| Liatris cylindracea | -80.76667 | 34.76667 | gbif | NA | 1144624572 |
| Liatris cylindracea | -79.80561 | 43.89389 | gbif | NA | 1899385146 |
| Liatris cylindracea | -83.72633 | 42.27087 | gbif | NA | 1989409051 |
| Liatris cylindracea | -92.83619 | 45.42608 | gbif | NA | 2265349156 |
| Liatris cylindracea | -95.38976 | 45.65024 | gbif | NA | 2265349165 |
| Liatris cylindracea | -91.74803 | 43.99779 | gbif | NA | 2265349170 |
| Liatris cylindracea | -92.72282 | 44.40976 | gbif | NA | 2265349268 |
| Liatris cylindracea | -95.38976 | 45.65024 | gbif | NA | 2265349376 |
| Liatris cylindracea | -92.66120 | 44.28943 | gbif | NA | 2265349481 |
| Liatris cylindracea | -93.16160 | 44.45830 | gbif | NA | 2265349647 |
| Liatris cylindracea | -93.28313 | 45.54365 | gbif | NA | 2265349799 |
| Liatris cylindracea | -92.61081 | 44.49954 | gbif | NA | 2265349807 |
| Liatris cylindracea | -93.49197 | 44.97475 | gbif | NA | 2265349841 |
| Liatris cylindracea | -93.28313 | 45.54365 | gbif | NA | 2265349855 |
| Liatris cylindracea | -92.99108 | 45.50656 | gbif | NA | 2265349865 |
| Liatris cylindracea | -95.38976 | 45.65024 | gbif | NA | 2265349960 |
| Liatris cylindracea | -94.94038 | 45.19606 | gbif | NA | 2265350037 |
| Liatris cylindracea | -94.24059 | 46.34845 | gbif | NA | 2265350074 |
| Liatris cylindracea | -93.26384 | 44.97996 | gbif | NA | 2265350154 |
| Liatris cylindracea | -93.59000 | 44.02000 | gbif | NA | 2265350197 |
| Liatris cylindracea | -92.72259 | 44.40985 | gbif | NA | 2265350334 |
| Liatris cylindracea | -93.16693 | 45.08003 | gbif | NA | 2265350357 |
| Liatris cylindracea | -93.26384 | 44.97996 | gbif | NA | 2265350396 |
| Liatris cylindracea | -93.47000 | 45.00000 | gbif | NA | 2265350467 |
| Liatris cylindracea | -95.37639 | 45.65025 | gbif | NA | 2265442926 |
| Liatris cylindracea | -94.20071 | 46.36871 | gbif | NA | 2265443303 |
| Liatris cylindracea | -83.53028 | 38.81083 | gbif | NA | 91660785 |
| Liatris cylindracea | -87.03444 | 33.05861 | gbif | NA | 91660797 |
| Liatris cylindracea | -92.83619 | 45.42608 | idigbio | NA | 021b6c1d-9b22-45e4-9cea-a26821e65910 |
| Liatris cylindracea | -91.31853 | 43.62916 | idigbio | 1919-08-01 | 039f4bde-2ab1-4561-a9af-1c850a42aab9 |
| Liatris cylindracea | -86.00100 | 38.86488 | idigbio | 1958-09-07 | 0923ee62-4829-4879-a6ae-d8cfec88e331 |
| Liatris cylindracea | -91.42281 | 43.76774 | idigbio | 1978-08-14 | 09848e55-039e-49b8-91f5-7c3cded80e1c |
| Liatris cylindracea | -90.57678 | 41.52914 | idigbio | NA | 0bab91db-f7db-4115-aebf-18bd0533cf25 |
| Liatris cylindracea | -91.42233 | 43.62329 | idigbio | 1978-08-15 | 0bbf443b-785b-4dde-8421-77c60fdfacac |
| Liatris cylindracea | -93.26384 | 44.97996 | idigbio | 1878-08-14 | 0fafba30-3764-4fd9-aed5-f3e98531721c |
| Liatris cylindracea | -91.99041 | 44.17096 | idigbio | 1980-08-15 | 103acddc-33b1-4ea4-a816-47eb335aea70 |
| Liatris cylindracea | -92.54583 | 44.52361 | idigbio | 1986-08-11 | 134b3de9-aa79-4e23-8e1e-1a8dd0149f68 |
| Liatris cylindracea | -83.72633 | 42.27087 | idigbio | 1861-08-23 | 13b34217-ea49-468e-8bfe-ccb255446415 |
| Liatris cylindracea | -92.08925 | 44.09956 | idigbio | 1994-07-26 | 153516e5-dcba-4e68-b6cb-2e087244260b |
| Liatris cylindracea | -89.46750 | 31.12278 | idigbio | 2011-10-31 | 15c9ead4-210a-41c1-921b-ba8aeb23fd81 |
| Liatris cylindracea | -84.65981 | 45.36937 | idigbio | 1977-07-12 | 1e31791c-986b-464b-b3f0-ddab33d5abae |
| Liatris cylindracea | -92.04933 | 44.04203 | idigbio | 1982-08-16 | 20960f79-70e2-4c12-b416-f35af7198cfd |
| Liatris cylindracea | -93.26384 | 44.97996 | idigbio | NA | 239b13e3-4551-4a79-8688-462cdf4c5300 |
| Liatris cylindracea | -93.19351 | 45.39232 | idigbio | 1989-08-15 | 2a1eb429-d266-4d2c-a175-97c04cd19ea8 |
| Liatris cylindracea | -81.83333 | 43.26667 | idigbio | 1978-09-01 | 2a826fb4-47bf-401f-bcf6-b0c42f3f4654 |
| Liatris cylindracea | -83.72633 | 42.27087 | idigbio | 1892-08-12 | 2a9abca5-0b70-479f-a360-af1dbb027b01 |
| Liatris cylindracea | -93.16160 | 44.45830 | idigbio | NA | 2a9d5de3-06a6-4f91-be67-40344700ca34 |
| Liatris cylindracea | -91.55129 | 43.63067 | idigbio | 1963-08-21 | 2c2f293c-b418-4a8f-9edb-f8fe14ea8a3e |
| Liatris cylindracea | -81.96000 | 43.21000 | idigbio | 2002-08-14 | 2cdf4ab4-a796-4671-bd37-0d7e6484a1d0 |
| Liatris cylindracea | -91.08437 | 41.33353 | idigbio | 1911-07-14 | 323e9ef9-9f91-47ef-b78f-c619162c7367 |
| Liatris cylindracea | -92.52110 | 44.56198 | idigbio | 1960-08-21 | 32cd4c8c-8b98-4b89-879f-bb3e620cc57c |
| Liatris cylindracea | -90.80709 | 41.45920 | idigbio | NA | 35857481-d738-443d-816e-c3c2f22cb7a2 |
| Liatris cylindracea | -92.26684 | 44.44941 | idigbio | 1892-08-20 | 3d9f82c3-26b3-4e8d-96a5-36ca3c3e693d |
| Liatris cylindracea | -81.83333 | 43.26667 | idigbio | 1978-09-01 | 3fd73215-55e8-45e9-863f-b3e5fc6a2c23 |
| Liatris cylindracea | -95.38976 | 45.65024 | idigbio | NA | 42e283a0-0ffe-441f-ac4d-e86352a4fcca |
| Liatris cylindracea | -88.66035 | 33.41494 | idigbio | 1992-05-27 | 4658101d-9236-40f6-98b2-2d8dc877abd2 |
| Liatris cylindracea | -93.26384 | 44.97996 | idigbio | NA | 49d68db4-b8ce-41a6-bba8-e18f0feb2225 |
| Liatris cylindracea | -92.99108 | 45.50656 | idigbio | NA | 49dcdb38-7af9-48ca-9d95-614b0e75471a |
| Liatris cylindracea | -93.16646 | 44.90516 | idigbio | 1909-08-07 | 4b18242d-e06a-48b2-8646-208e536b65a5 |
| Liatris cylindracea | -86.03300 | 42.60900 | idigbio | 1991-08-08 | 5260420f-c130-4938-90ac-c8cffb753ba4 |
| Liatris cylindracea | -91.74803 | 43.99779 | idigbio | NA | 5349f5b8-36c9-4cb3-aa1b-b9c2f0133453 |
| Liatris cylindracea | -94.94038 | 45.19606 | idigbio | NA | 5364a7e5-d11d-4e9e-94a1-3a95b0eb2f80 |
| Liatris cylindracea | -90.57678 | 41.52914 | idigbio | NA | 5805d2a2-ab42-4406-987e-30a20880f3c2 |
| Liatris cylindracea | -95.38976 | 45.65024 | idigbio | NA | 5e2d6af4-9b80-41e3-9461-1a1f5e011bd2 |
| Liatris cylindracea | -89.64108 | 41.35768 | idigbio | 2012-08-24 | 61449c5e-3214-4bbe-9545-2aa02ad6c835 |
| Liatris cylindracea | -81.86667 | 46.00000 | idigbio | 1981-08-15 | 65596114-c374-44cb-a0f5-c785a1fd5fae |
| Liatris cylindracea | -92.54074 | 44.49194 | idigbio | 1988-06-15 | 673d05ee-a10f-40d5-907e-d75b9f1de5eb |
| Liatris cylindracea | -104.96147 | 39.73221 | idigbio | 1993-08-17 | 6740f27b-3023-4b34-897b-c64b82a65f7b |
| Liatris cylindracea | -81.83333 | 43.26667 | idigbio | 1978-09-01 | 6bdf0388-5175-4e0a-9198-e40fd2904e92 |
| Liatris cylindracea | -83.30227 | 42.51821 | idigbio | NA | 6c162c78-3483-40fb-86a7-83bac5adac7a |
| Liatris cylindracea | -95.38976 | 45.65024 | idigbio | NA | 6c70a145-7efb-419f-a0ea-02503124143a |
| Liatris cylindracea | -92.61081 | 44.49954 | idigbio | NA | 6fb0eac5-7072-40ad-a4d1-adf9a0d98338 |
| Liatris cylindracea | -92.79092 | 44.84076 | idigbio | 1987-09-30 | 741ead93-4cdc-42d6-aeea-f3a3ea992436 |
| Liatris cylindracea | -93.47000 | 45.00000 | idigbio | NA | 765464bd-e157-4595-9cd7-6f46cbf76c86 |
| Liatris cylindracea | -83.61299 | 42.24115 | idigbio | 1917-09-03 | 786e298b-4fff-4379-949d-7217a3cabf48 |
| Liatris cylindracea | -91.31256 | 43.54390 | idigbio | 1936-08-20 | 7984f7d7-bb49-462d-9b73-d00f0324e56c |
| Liatris cylindracea | -83.20592 | 43.97216 | idigbio | 1951-08-09 | 7d71d2a3-a765-457b-b52d-987b101577bc |
| Liatris cylindracea | -88.30687 | 42.23804 | idigbio | 2011-08-03 | 815f35ad-4b0f-49c4-a99e-dcfa11be1585 |
| Liatris cylindracea | -92.74229 | 44.63663 | idigbio | 1993-08-02 | 8853b35e-736e-4b77-8cb6-569cfad0a4ca |
| Liatris cylindracea | -91.42269 | 43.75640 | idigbio | 1997-08-07 | 8c48b775-d4a2-4332-b1d9-68bfdcc72dc7 |
| Liatris cylindracea | -90.55552 | 41.54769 | idigbio | 1889-07-07 | 8f42912a-cf30-4afc-830f-5598b79f22ee |
| Liatris cylindracea | -86.01568 | 42.52116 | idigbio | 1939-07-22 | 8fac66f4-91f3-421a-82da-6d425901b846 |
| Liatris cylindracea | -91.08168 | 41.32893 | idigbio | 1910-10-01 | 9070fedd-e94e-4339-bd7b-cde0a538dc6d |
| Liatris cylindracea | -84.40077 | 44.50095 | idigbio | 1953-07-25 | 92441d82-8af3-4635-86a5-8fe986dc5eed |
| Liatris cylindracea | -71.09533 | 42.52565 | idigbio | 1880-08-21 | 9375e4f5-212e-4f1c-bfcc-cc9a0b45096a |
| Liatris cylindracea | -89.61468 | 41.35802 | idigbio | 2010-07-07 | 96c2fda8-8a2e-403f-82f9-c5dadeb20c1f |
| Liatris cylindracea | -93.13361 | 35.27833 | idigbio | 1972-10-14 | 9812a3fb-fe31-4568-99a4-726dad5a9164 |
| Liatris cylindracea | -86.01568 | 42.52116 | idigbio | 1939-08-07 | 9b953e07-596d-4fdd-b405-ffe0403e6431 |
| Liatris cylindracea | -89.46750 | 31.12278 | idigbio | 2011-10-31 | 9bce918d-84b7-4453-b6eb-3c176453a11b |
| Liatris cylindracea | -93.66378 | 45.45102 | idigbio | 2001-08-28 | 9cd07271-ccfa-46fa-9813-daa2ff223aa7 |
| Liatris cylindracea | -94.24059 | 46.34845 | idigbio | NA | a39cb742-426d-4f51-af35-e3199af1b439 |
| Liatris cylindracea | -88.09183 | 41.81472 | idigbio | 2012-09-14 | a6bdfa20-fdea-4e7d-8e71-6c91db2cc44f |
| Liatris cylindracea | -90.95013 | 40.23004 | idigbio | NA | a7b08fba-3408-4fb1-ad95-370130bb0e52 |
| Liatris cylindracea | -93.49197 | 44.97475 | idigbio | NA | a80e154c-00a3-4c82-93cd-bbe76493499a |
| Liatris cylindracea | -91.28287 | 43.50806 | idigbio | 1899-07-29 | a86b1039-3d65-4d56-9b8e-b21bae479005 |
| Liatris cylindracea | -93.16693 | 45.08003 | idigbio | NA | ab08ecbd-0ae2-450a-a980-41bb722bd957 |
| Liatris cylindracea | -93.72596 | 45.46555 | idigbio | 1998-08-14 | acfc182b-2c1a-4cd3-bf3e-95c3f195d957 |
| Liatris cylindracea | -90.88104 | 41.46936 | idigbio | 1892-08-28 | adc2c1b0-2635-40f0-9e89-376767dbf0ac |
| Liatris cylindracea | -82.75000 | 46.78333 | idigbio | 1981-08-19 | af530cd6-8efd-4ec6-896b-dd1d869a73d6 |
| Liatris cylindracea | -92.77505 | 44.85476 | idigbio | 1985-06-02 | afff1832-5d50-484b-995a-2f857a2851f8 |
| Liatris cylindracea | -92.38209 | 43.43849 | idigbio | 1978-07-28 | b0202c66-fd93-4f67-83cd-3d4ea9e42cbf |
| Liatris cylindracea | -83.72633 | 42.27087 | idigbio | 1861-07-31 | b5b89b61-79b2-4c4b-84b9-d90b8771ac02 |
| Liatris cylindracea | -90.62689 | 41.55328 | idigbio | 1915-08-30 | b9d3f69b-af0b-4dd4-afcc-4b89b6aa433d |
| Liatris cylindracea | -83.72684 | 42.29218 | idigbio | NA | ba55b9c5-0440-4675-b31e-8848c46a183e |
| Liatris cylindracea | -87.59394 | 41.79420 | idigbio | 1893-08-30 | c1e013cb-56e7-42df-bfea-6149d3c80be3 |
| Liatris cylindracea | -83.72633 | 42.27087 | idigbio | NA | c43fd15a-fa5a-442e-855e-dba7e20ccbf0 |
| Liatris cylindracea | -87.59237 | 41.56148 | idigbio | 1963-08-15 | c48ff884-3840-47d7-8e41-26f48e756986 |
| Liatris cylindracea | -92.81944 | 44.80028 | idigbio | 1988-07-26 | c5d18b51-c0e0-41a5-89ff-18150389fdc4 |
| Liatris cylindracea | -92.01953 | 43.76751 | idigbio | 1994-07-29 | cac2e14b-a141-46a2-8752-295077781dae |
| Liatris cylindracea | -96.44879 | 47.73852 | idigbio | 1979-08-15 | d2dffc26-23f6-4ada-80bd-089416868acb |
| Liatris cylindracea | -92.74229 | 44.63663 | idigbio | 1993-08-04 | d38599cb-2e1b-45ec-b36b-7de887b9dda9 |
| Liatris cylindracea | -83.74886 | 42.27913 | idigbio | NA | d7d0aa52-634f-47d6-b075-943085e95051 |
| Liatris cylindracea | -91.36237 | 43.62304 | idigbio | 1966-07-29 | d95fdaac-7492-4d9e-bae4-19697f5670fa |
| Liatris cylindracea | -83.11906 | 42.68765 | idigbio | 1925-08-26 | da2c61e9-70ae-4e10-a044-500e8c1da11c |
| Liatris cylindracea | -91.63931 | 44.04996 | idigbio | 1888-09-13 | dce4d4a7-953b-42a5-b090-2bb1c041b434 |
| Liatris cylindracea | -91.42269 | 43.75640 | idigbio | 1998-08-08 | de5a1e0a-25cf-4516-b66d-7aae1d8f1422 |
| Liatris cylindracea | -93.28313 | 45.54365 | idigbio | NA | e378dd93-1ad5-48b5-b6ae-ddd51ad69a21 |
| Liatris cylindracea | -93.13361 | 35.27833 | idigbio | 1972-10-14 | e5f12f01-0eed-4e86-ae46-645186cd1978 |
| Liatris cylindracea | -93.28313 | 45.54365 | idigbio | NA | e6ac07ba-e527-4b11-afc4-ef661015501f |
| Liatris cylindracea | -92.66120 | 44.28943 | idigbio | NA | e76aff04-082e-47a7-aa84-44ffa1195737 |
| Liatris cylindracea | -91.99041 | 44.17096 | idigbio | 1986-07-31 | e9e8a887-f89a-4fdf-878e-9fd215d73c95 |
| Liatris cylindracea | -92.46120 | 38.14800 | idigbio | 2010-10-16 | eabfcb0f-f8cc-4390-9b8c-c55208f45dd9 |
| Liatris cylindracea | -95.24502 | 45.44867 | idigbio | 1982-07-29 | eb6a8b19-eaf3-4f3b-8184-00668eceb635 |
| Liatris cylindracea | -92.52110 | 44.56198 | idigbio | 1982-07-31 | f7bc3b78-9217-4a1d-8901-a316455ac5ad |
| Liatris cylindracea | -93.59000 | 44.02000 | idigbio | NA | fc40ac35-8362-4ef4-be9c-434779d499f0 |
| Liatris cylindracea | -91.39316 | 43.95302 | idigbio | 1943-08-25 | fca3412f-8420-43c8-b4b2-f6a18945d2b5 |
| Liatris cylindracea | -92.36995 | 44.41148 | idigbio | 1883-08-14 | fdff27f2-68c0-4ec1-88f1-f1f3c177e009 |
Occurrence Data Cleaning
Modified based on script written by Charlotte Germain-Aubrey.
Modified and created by ML Gaynor.
Load Packages
library(dplyr)
library(tidyr)
library(rjson)
library(RCurl)
library(raster)
library(sp)Load data files
The datafiles for this demo are stored in “data/cleaning_demo/”.
raw.data <- read.csv("data/cleaning_demo/SampleFL-data.csv")
head(raw.data)## species lat long year
## 1 Asclepias_curtissii 26.08294 -81.40069 1943
## 2 Asclepias_curtissii 28.29933 -70.70104 1955
## 3 Asclepias_curtissii 35.30096 -82.43857 1923
## 4 Asclepias_curtissii 26.14614 -80.45289 \\N
## 5 Asclepias_curtissii 26.57758 -81.92146 1972
## 6 Asclepias_curtissii 26.64141 -80.43702 1981Exploring the datafile
How many species are included in this file?
(species_count <- raw.data %>%
group_by(species) %>%
tally())## # A tibble: 5 x 2
## species n
## <fct> <int>
## 1 Asclepias_curtissii 38
## 2 Asimina_obovata 16
## 3 Pinus_australis 60
## 4 Pinus_palustris 14
## 5 Pityothamnus_obovatus 14Taxonomic Cleaning
Why?
Lots of bad names due to misspelling, synonymns, and more.
How?
iPlant TNRS Tool.
In addition to accessing the iPlant TNRS Tool in R, you can also use the website to access the database. In this tutorial, we only focus on checking synoymns using one tool, however there are many other tools to check taxonomy. For example, taxize can be used to accesss taxomny databases including Encylopedia of Life (eol), Taxonomic Name Resolution Service (tnrs), Integrated Taxonomic Information Service (itis), and many more.
Using the iPlant TNRS tool
Transform the species names list
First, we need to subset the names from the list we made above.
# Subset from the list we made above
(names <- species_count$species)## [1] Asclepias_curtissii Asimina_obovata Pinus_australis
## [4] Pinus_palustris Pityothamnus_obovatus
## 5 Levels: Asclepias_curtissii Asimina_obovata ... Pityothamnus_obovatusNext, remove the underscores between the genus and species name, replace with spaces.
(names<-gsub('_',' ',names))## [1] "Asclepias curtissii" "Asimina obovata" "Pinus australis"
## [4] "Pinus palustris" "Pityothamnus obovatus"Turn the list into a string by adding commas.
(names<-paste(names,collapse=','))## [1] "Asclepias curtissii,Asimina obovata,Pinus australis,Pinus palustris,Pityothamnus obovatus"Using the package RCurl makes the string URL-encoded.
names<-curlEscape(names)Query the API
Using the packages rjson and RCurl we can query the Application programming interface (API) for the iPlant TNRS tool.
We have to set an object equal to the API url address.
tnrs.api<-'http://tnrs.iplantc.org/tnrsm-svc'Next, we send a request to the address specified above.
url<-paste(tnrs.api,'/matchNames?retrieve=best&names=',names,sep='')
tnrs.json<-getURL(url) Process the results
The response is saved as tnrs.json above. Now, the response needs to be converted to a readable format from JSON.
tnrs.results<-fromJSON(tnrs.json)Next, we need to summarize the names and change the format to match our raw data. First we isolate the names submitted and the found accepted name. Next, we replace the space in between the genus and species names with an underscore. Then, we convert the data file to a dataframe. Finally, we rename the columns to match the names in raw.data.
corrected_names<-sapply(tnrs.results[[1]], function(x) c(x$nameSubmitted,x$acceptedName))
corrected_names <- gsub(" ", "_", corrected_names)
corrected_names<-as.data.frame(t(corrected_names),stringsAsFactors=FALSE)
names(corrected_names) <- c("species", "new")
corrected_names## species new
## 1 Asclepias_curtissii Asclepias_curtissii
## 2 Asimina_obovata Asimina_obovata
## 3 Pinus_australis Pinus_palustris
## 4 Pinus_palustris Pinus_palustris
## 5 Pityothamnus_obovatus Asimina_obovataCorrecting names
We now need to correct the names in the raw data file. To do this, we are going to merge the datasets, so we can save the new name with all the information in our raw data file.
merged_datasets <- merge(raw.data, corrected_names, by = "species")
head(merged_datasets)## species lat long year new
## 1 Asclepias_curtissii 26.08294 -81.40069 1943 Asclepias_curtissii
## 2 Asclepias_curtissii 28.29933 -70.70104 1955 Asclepias_curtissii
## 3 Asclepias_curtissii 35.30096 -82.43857 1923 Asclepias_curtissii
## 4 Asclepias_curtissii 26.14614 -80.45289 \\N Asclepias_curtissii
## 5 Asclepias_curtissii 26.57758 -81.92146 1972 Asclepias_curtissii
## 6 Asclepias_curtissii 26.64141 -80.43702 1981 Asclepias_curtissiiDate Cleaning
In addition to taxonmy fixes, we need to check the format of other columns, like dates. If you noticed above, when the year is missing there is an odd symbol. Lets replace that symbol with “NA”
merged_datasets$year <- gsub("\\N", NA, merged_datasets$year)Is there any other issues?
Visualize the year and data associated.
(year_count <- merged_datasets %>%
group_by(year) %>%
tally())## # A tibble: 23 x 2
## year n
## <chr> <int>
## 1 1896 1
## 2 1901 4
## 3 1923 6
## 4 1932 1
## 5 1943 1
## 6 1944 1
## 7 1955 3
## 8 1965 2
## 9 1972 2
## 10 1974 2
## # … with 13 more rowsLocation Cleaning
Precision
How precise is the locality information?
Precision is influenced by the number of decimal places associated with latitude and longitude. See below how degree and distance are related.| places | degree | distance |
|---|---|---|
| 0 | 1.0000 | 111 km |
| 1 | 0.1000 | 11.1 km |
| 2 | 0.0100 | 1.11 km |
| 3 | 0.0010 | 111 m |
| 4 | 0.0001 | 11.1 m |
Here we are going to round to two decimal points.
(merged_datasets$lat <- round(merged_datasets$lat, digits = 2))## [1] 26.08 28.30 35.30 26.15 26.58 26.64 27.08 27.34 27.38 29.47
## [11] 27.48 27.49 27.70 27.90 27.91 27.96 28.06 28.30 28.30 28.51
## [21] 28.55 28.71 28.77 29.07 29.21 29.47 29.61 29.98 26.58 27.49
## [31] 27.91 28.51 29.07 29.61 0.00 18.51 -28.55 28.71 28.71 0.00
## [41] 28.85 29.07 28.71 29.21 29.28 29.47 29.61 29.68 29.98 26.95
## [51] 27.91 28.51 29.28 29.98 28.30 28.51 28.55 28.71 28.71 28.00
## [61] 28.77 29.07 29.21 29.28 29.47 29.59 29.61 29.68 29.73 29.80
## [71] 29.90 29.91 29.95 29.98 29.99 30.02 30.04 30.15 0.00 30.23
## [81] 30.23 30.24 30.32 30.33 30.41 30.42 30.45 30.46 30.49 30.50
## [91] 30.58 30.61 30.61 30.61 30.61 30.67 30.70 30.80 30.87 29.61
## [101] 29.68 29.73 29.80 30.02 30.58 30.80 30.87 19.73 19.80 19.90
## [111] 19.91 18.95 19.98 19.99 26.58 26.90 26.95 27.19 27.34 27.39
## [121] 27.48 27.49 27.70 27.90 27.91 27.96 28.06 28.10 26.95 27.34
## [131] 27.38 27.48 27.70 27.91 27.96 28.06 28.30 28.30 28.51 28.55
## [141] 25.51 25.55merged_datasets$long <- round(merged_datasets$long, digits = 2)Removing impossible points
Prior to the next step we have 142 points, after we have 137.
merged_datasets <- merged_datasets %>%
filter(lat != 0, long != 0)Remove points that are botanical gardens.
First, load the data file of the botanical gardens in florida.
bg.points <- read.csv("data/cleaning_demo/BotanicalGardensFloridaCoordinates.csv", header=TRUE)Next, we are going to filter the merged_dataset to exclude any coordinates that are in the bg.points dataset.
merged_datasets <- merged_datasets %>%
filter(!lat %in% bg.points$Lat & !long %in% bg.points$Long)Removing duplicates
We currently have 137 observations. Once only unique records are obtained, we have 121 observation.
merged_datasets.unique <- merged_datasets %>%
distinct
head(merged_datasets.unique) ## species lat long year new
## 1 Asclepias_curtissii 26.08 -81.40 1943 Asclepias_curtissii
## 2 Asclepias_curtissii 28.30 -70.70 1955 Asclepias_curtissii
## 3 Asclepias_curtissii 35.30 -82.44 1923 Asclepias_curtissii
## 4 Asclepias_curtissii 26.58 -81.92 1972 Asclepias_curtissii
## 5 Asclepias_curtissii 26.64 -80.44 1981 Asclepias_curtissii
## 6 Asclepias_curtissii 27.08 -80.40 2000 Asclepias_curtissiiTrimming to the desired area
The desired area is Florida.
Using the package raster we are able to create a raster layer of the USA. If you want to download other countries using getData, check out the avaliable maps. Next, we can subset for Florida. State names are saved as “NAME_1” and county names are saved as “NAME_2” in the map file.
# name = 'GADM' which is Database of Global Administrative Areas
# level = 2 gives the level of subdivision (States)
usa <- raster::getData('GADM', country='USA', level=2)
# subset
florida <- subset(usa, NAME_1=="Florida")Next, convert merged dataset unique into a spatial file.
xy_data <- data.frame(x = merged_datasets.unique$long, y = merged_datasets.unique$lat)
coordinates(xy_data) <- ~ x + y
proj4string(xy_data) <- crs("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")Extract the values of the points for the Florida polygon. Then, recombine everything to gather all data. Next, filter for only the points in Florida, select the columns needed for MaxEnt input, and rename the new names as simply ‘species’.
over <- over(xy_data, florida)
total <- cbind(merged_datasets.unique, over)
florida_points <- total %>%
filter(NAME_1 == "Florida") %>%
dplyr::select(new, lat, long) %>%
rename(species = new)
#rename(replace = c("species" = "new")) # AEM EDIT
head(florida_points)## species lat long
## 1 Asclepias_curtissii 26.08 -81.40
## 4 Asclepias_curtissii 26.58 -81.92
## 5 Asclepias_curtissii 26.64 -80.44
## 6 Asclepias_curtissii 27.08 -80.40
## 7 Asclepias_curtissii 27.38 -80.44
## 8 Asclepias_curtissii 27.48 -82.36Visualize the points using ggplot2.
library(ggplot2)
states <- map_data("state")
florida <- subset(states, region == "florida" )
ggplot()+
geom_polygon(florida, mapping = aes(x = long, y = lat),color = "black", fill = "gray", size = .2) +
geom_point(florida_points, mapping = aes(x = long, y = lat, col = species ))
Finally, write a csv for the maxent steps.
#We need a csv with only species, lat, long for MaxEnt input
write.csv(florida_points, "data/cleaning_demo/MaxEntPointsInput_cleaned.csv", row.names = FALSE)Climate Processing
Original script by Charlotte Germain-Aubrey.
Modified and created by ML Gaynor.
Load Packages
library(maptools)
library(raster)
library(rgdal)
library(sp)
library(maps)
library(mapproj)Load bioclim layers
alt_l <- raster("data/climate_processing/Bioclim/alt.bil")
bio1_l <- raster("data/climate_processing/Bioclim/bio1.bil")
bio2_l <- raster("data/climate_processing/Bioclim/bio2.bil")
bio3_l <- raster("data/climate_processing/Bioclim/bio3.bil")
bio4_l <- raster("data/climate_processing/Bioclim/bio4.bil")
bio5_l <- raster("data/climate_processing/Bioclim/bio5.bil")
bio6_l <- raster("data/climate_processing/Bioclim/bio6.bil")
bio7_l <- raster("data/climate_processing/Bioclim/bio7.bil")
bio8_l <- raster("data/climate_processing/Bioclim/bio8.bil")
bio9_l <- raster("data/climate_processing/Bioclim/bio9.bil")
bio10_l <- raster("data/climate_processing/Bioclim/bio10.bil")
bio11_l <- raster("data/climate_processing/Bioclim/bio11.bil")
bio12_l <- raster("data/climate_processing/Bioclim/bio12.bil")
bio13_l <- raster("data/climate_processing/Bioclim/bio13.bil")
bio14_l <- raster("data/climate_processing/Bioclim/bio14.bil")
bio15_l <- raster("data/climate_processing/Bioclim/bio15.bil")
bio16_l <- raster("data/climate_processing/Bioclim/bio16.bil")
bio17_l <- raster("data/climate_processing/Bioclim/bio17.bil")
bio18_l <- raster("data/climate_processing/Bioclim/bio18.bil")
bio19_l <- raster("data/climate_processing/Bioclim/bio19.bil")Read map of desired extent
There are many ways to determine your desired extent -
for example, convex hulls are often reccomended. We reccomend you refer to ENM literature before selecting the extent for your own project.
FL <- rgdal::readOGR("data/climate_processing/FL/FLstate2.shp")## OGR data source with driver: ESRI Shapefile
## Source: "/Users/shellygaynor/Dropbox/Workshops/Botany2019WorkshopNotes/ENM Material - Gaynor/iDigBio_Rbased_Demo/data/climate_processing/FL/FLstate2.shp", layer: "FLstate2"
## with 200 features
## It has 13 fieldsCrop bioclim layers to desired extent
Here is the starting layer.
plot(alt_l)
First, mask the bioclim layer with Florida. Notice that the plot is blank because the map is masked.
alt <- mask(alt_l, FL)
plot(alt)
Next, crop the bioclim layer to Florida’s extent.
alt <- crop(alt, extent(FL))
plot(alt)
Finally, write an .asc cropped file to be used for MaxEnt.
writeRaster(alt, "data/climate_processing/PresentLayers/alt.asc", format="ascii")Repeat with all additional layers.
bio1 <- mask(bio1_l, FL)
bio1 <- crop(bio1, extent(FL))
#writeRaster(bio1, "data/climate_processing/PresentLayers/bio1.asc", format="ascii")
bio2 <- mask(bio2_l, FL)
bio2 <- crop(bio2, extent(FL))
#writeRaster(bio2, "data/climate_processing/PresentLayers/bio2.asc", format="ascii")
bio3 <- mask(bio3_l, FL)
bio3 <- crop(bio3, extent(FL))
#writeRaster(bio3, "data/climate_processing/PresentLayers/bio3.asc", format="ascii")
bio4 <- mask(bio4_l, FL)
bio4 <- crop(bio4, extent(FL))
#writeRaster(bio4, "data/climate_processing/PresentLayers/bio4.asc", format="ascii")
bio5 <- mask(bio5_l, FL)
bio5 <- crop(bio5, extent(FL))
#writeRaster(bio5, "data/climate_processing/PresentLayers/bio5.asc", format="ascii")
bio6 <- mask(bio6_l, FL)
bio6 <- crop(bio6, extent(FL))
#writeRaster(bio6, "data/climate_processing/PresentLayers/bio6.asc", format="ascii")
bio7 <- mask(bio7_l, FL)
bio7 <- crop(bio7, extent(FL))
#writeRaster(bio7, "data/climate_processing/PresentLayers/bio7.asc", format="ascii")
bio8 <- mask(bio8_l, FL)
bio8 <- crop(bio8, extent(FL))
#writeRaster(bio8, "data/climate_processing/PresentLayers/bio8.asc", format="ascii")
bio9 <- mask(bio9_l, FL)
bio9 <- crop(bio9, extent(FL))
#writeRaster(bio9, "data/climate_processing/PresentLayers/bio9.asc", format="ascii")
bio10 <- mask(bio10_l, FL)
bio10 <- crop(bio10, extent(FL))
#writeRaster(bio10, "data/climate_processing/PresentLayers/bio10.asc", format="ascii")
bio11 <- mask(bio11_l, FL)
bio11 <- crop(bio11, extent(FL))
#writeRaster(bio11, "data/climate_processing/PresentLayers/bio11.asc", format="ascii")
bio12 <- mask(bio12_l, FL)
bio12 <- crop(bio12, extent(FL))
#writeRaster(bio12, "data/climate_processing/PresentLayers/bio12.asc", format="ascii")
bio13 <- mask(bio13_l, FL)
bio13 <- crop(bio13, extent(FL))
#writeRaster(bio13, "data/climate_processing/PresentLayers/bio13.asc", format="ascii")
bio14 <- mask(bio14_l, FL)
bio14 <- crop(bio14, extent(FL))
#writeRaster(bio14, "data/climate_processing/PresentLayers/bio14.asc", format="ascii")
bio15 <- mask(bio15_l, FL)
bio15 <- crop(bio15, extent(FL))
#writeRaster(bio15, "data/climate_processing/PresentLayers/bio15.asc", format="ascii")
bio16 <- mask(bio16_l, FL)
bio16 <- crop(bio16, extent(FL))
#writeRaster(bio16, "data/climate_processing/PresentLayers/bio16.asc", format="ascii")
bio17 <- mask(bio17_l, FL)
bio17 <- crop(bio17, extent(FL))
#writeRaster(bio17, "data/climate_processing/PresentLayers/bio17.asc", format="ascii")
bio18 <- mask(bio18_l, FL)
bio18 <- crop(bio18, extent(FL))
#writeRaster(bio18, "data/climate_processing/PresentLayers/bio18.asc", format="ascii")
bio19 <- mask(bio19_l, FL)
bio19 <- crop(bio19, extent(FL))
#writeRaster(bio19, "data/climate_processing/PresentLayers/bio19.asc", format="ascii")Selecting layers for MaxEnt
We only want to include layers that are not highly correlated. To assess which layers to include, we will look at the pearson correlation coefficient among layers.
#### Now let's do a correlation analysis to try and reduce the number of layers for MaxEnt
stack <- stack(bio1, bio2, bio3, bio4, bio5, bio6, bio7, bio8, bio9, bio10, bio11, bio12, bio13, bio14, bio15, bio16, bio17, bio18, bio19)
corr <- layerStats(stack, 'pearson', na.rm=TRUE)
c <- corr$`pearson correlation coefficient`
write.csv(c, "data/climate_processing/correlationBioclim.csv")Option 1
Open this file in Excel to view the values. It is more or less up to you which layers are removed, as long as the resulting layer set is lowly correlated.
Option 2
If you do not want to decide which layers you should keep or remove, you can use the following tool. This tool is described as follows: “The absolute values of pair-wise correlations are considered. If two variables have a high correlation, the function looks at the mean absolute correlation of each variable and removes the variable with the largest mean absolute correlation.” - This shows you which layers to remove.
library(caret)
c <- data.matrix(read.csv("data/climate_processing/correlationBioclim.csv", header = TRUE, row.names = 1,
sep = ","))
c <- abs(c)
envtCor <- findCorrelation(c, cutoff = 0.80, names = TRUE, exact = TRUE)
sort(envtCor)## [1] "bio1" "bio10" "bio11" "bio13" "bio15" "bio16" "bio17" "bio19" "bio4"
## [10] "bio6" "bio7"Ecological Analysis using Points
This is based off scripts created by Hannah Owens and James Watling, and Anthony Melton.
Modified and created by ML Gaynor.
Load Packages
library(raster)
library(rgdal)
library(ENMTools)
library(dismo)
library(RStoolbox)
library(hypervolume)
library(ggplot2)
library(dplyr)
library(tidyr)
library(gtools)
library(ggbiplot)Load Datafiles
Species occurrence records
This is the file you made at the end of the Occurrence_Data_Cleaning.R script.
florida_points <- read.csv("data/cleaning_demo/MaxEntPointsInput_cleaned.csv")Subset occurrence records for each species
Asclepias_curtissii <- florida_points %>%
filter(species == "Asclepias_curtissii")
Asimina_obovata <- florida_points %>%
filter(species == "Asimina_obovata")
Pinus_palustris <- florida_points %>%
filter(species == "Pinus_palustris")Raster layers
These files were made in the ClimateProcesssing.R script.
# Raster stack
list <- list.files("data/climate_processing/PresentLayers/", full.names = T, recursive = FALSE)
list <- mixedsort(sort(list))
envtStack <- stack(list)ENM - Realized niche
For each occurrence record, extract the value for each bioclim variables using the package raster.
ptExtract_Asclepias_curtissii <- raster::extract(envt.subset, Asclepias_curtissii[3:2])
ptExtract_Asimina_obovata <- raster::extract(envt.subset, Asimina_obovata[3:2])
ptExtract_Pinus_palustris <- raster::extract(envt.subset, Pinus_palustris[3:2])Convert the extracted information for all three species and combined. I wrote a quick function to convert the files to dataframes.
# Set-up the function
convert_ptExtract <- function(value, name){
# convert ptExtract to a data frame
value_df <- as.data.frame(value)
# add a column with species name
value_df_DONE <- value_df %>%
mutate(species = name)
# return data frame
return(value_df_DONE)
}
# Apply the function
ptExtract_Asclepias_curtissii_df <- convert_ptExtract(ptExtract_Asclepias_curtissii, "Asclepias_curtissii")
ptExtract_Asimina_obovata_df <- convert_ptExtract(ptExtract_Asimina_obovata, "Asimina_obovata")
ptExtract_Pinus_palustris_df <- convert_ptExtract(ptExtract_Pinus_palustris, "Pinus_palustris")
# Combined the three converted files and remove any values where NA appears
pointsamples_combined <- rbind(ptExtract_Asclepias_curtissii_df,ptExtract_Asimina_obovata_df, ptExtract_Pinus_palustris_df)
pointsamples_combined <- pointsamples_combined %>%
drop_na(bio2, bio3, bio5, bio8, bio9, bio12, bio14, bio18)PCA
Create two dataframes.
data.bioclim <- pointsamples_combined[, 1:8]
data.species <- pointsamples_combined[, 9]Using only the bioclim columns to run the principal components analysis.
data.pca <- prcomp(data.bioclim, scale. = TRUE) Understanding the PCA - Optional
library(factoextra)
library(FactoMineR)When you use the command prcomp your loading variables show up as rotational variables. Thanks to a really great answer on stack overflow you can even convert the rotational variable to show the relative contribution.
loadings <- data.pca$rotation
#summary(loadings)There are two options to convert the loading to show the relative contribution, they both give the same answer so either can be used.
loadings_relative_A <- t(t(abs(loadings))/rowSums(t(abs(loadings))))*100
summary(loadings_relative_A)## PC1 PC2 PC3 PC4
## Min. : 2.741 Min. : 1.553 Min. : 0.702 Min. : 0.07019
## 1st Qu.: 9.919 1st Qu.: 2.833 1st Qu.: 4.693 1st Qu.: 4.75169
## Median :13.826 Median : 5.577 Median : 8.274 Median :11.75173
## Mean :12.500 Mean :12.500 Mean :12.500 Mean :12.50000
## 3rd Qu.:16.644 3rd Qu.:19.293 3rd Qu.:17.401 3rd Qu.:17.78491
## Max. :17.173 Max. :36.658 Max. :31.438 Max. :28.34724
## PC5 PC6 PC7 PC8
## Min. : 0.2921 Min. : 1.572 Min. : 1.967 Min. : 3.251
## 1st Qu.: 5.1674 1st Qu.: 3.075 1st Qu.: 4.478 1st Qu.: 5.486
## Median :10.3572 Median : 6.785 Median : 8.171 Median : 8.745
## Mean :12.5000 Mean :12.500 Mean :12.500 Mean :12.500
## 3rd Qu.:22.4759 3rd Qu.:18.127 3rd Qu.:20.815 3rd Qu.:17.589
## Max. :25.4017 Max. :36.447 Max. :28.451 Max. :29.207loadings_relative_B <- sweep(x = abs(loadings), MARGIN = 2, STATS = colSums(abs(loadings)), FUN = "/")*100
summary(loadings_relative_B)## PC1 PC2 PC3 PC4
## Min. : 2.741 Min. : 1.553 Min. : 0.702 Min. : 0.07019
## 1st Qu.: 9.919 1st Qu.: 2.833 1st Qu.: 4.693 1st Qu.: 4.75169
## Median :13.826 Median : 5.577 Median : 8.274 Median :11.75173
## Mean :12.500 Mean :12.500 Mean :12.500 Mean :12.50000
## 3rd Qu.:16.644 3rd Qu.:19.293 3rd Qu.:17.401 3rd Qu.:17.78491
## Max. :17.173 Max. :36.658 Max. :31.438 Max. :28.34724
## PC5 PC6 PC7 PC8
## Min. : 0.2921 Min. : 1.572 Min. : 1.967 Min. : 3.251
## 1st Qu.: 5.1674 1st Qu.: 3.075 1st Qu.: 4.478 1st Qu.: 5.486
## Median :10.3572 Median : 6.785 Median : 8.171 Median : 8.745
## Mean :12.5000 Mean :12.500 Mean :12.500 Mean :12.500
## 3rd Qu.:22.4759 3rd Qu.:18.127 3rd Qu.:20.815 3rd Qu.:17.589
## Max. :25.4017 Max. :36.447 Max. :28.451 Max. :29.207Plotting the PCA
First, I made a theme to change the background of the plot. Next, I changed the plot margins and the text size.
theme <- theme(panel.background = element_blank(),
panel.border=element_rect(fill=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
strip.background=element_blank(),
axis.ticks=element_line(colour="black"),
plot.margin=unit(c(1,1,1,1),"line"),
axis.text = element_text(size = 12),
legend.text = element_text(size = 12),
legend.title = element_text(size = 12),
text = element_text(size = 12))Next, ggbiplot where obs.scale indicates the scale factor to apply to observation, var.scale indicates the scale factor to apply to variables, ellipse as TRUE draws a normal data ellipse for each group, and circle as TRUE draws a correlation circle.
g <- ggbiplot(data.pca, obs.scale = 1, var.scale = 1, groups = data.species, ellipse = TRUE, circle = TRUE)
g <- g + theme
g <- g + scale_colour_manual(values = c("#73dfff", "#ff8700", "#7a8ef5"))
g <- g + theme(legend.direction = 'horizontal', legend.position = 'bottom')
g
***
Multivariate environment similarity surface (MESS) analysis and mahalanobis distances
For MESS, the more negative the score, the more likely you are to project into novel environments. For mahalanobis distances, zero equals completely overlapping. Comparatively, the higher the distance, the more likely you are to project into an environment that falls outside the range of the training region, leading to unreliable projections.
Read layer files
Training region layers
env.files.c <- list.files(path = "data/climate_processing/PresentLayers/", pattern = ".asc", full.names = TRUE) #Always check for tiff or asc
current <- stack(env.files.c)
names(current) <- c("alt", "bio1", "bio10", "bio11", "bio12", "bio13", "bio14", "bio15", "bio16", "bio17", "bio18", "bio19", "bio2", "bio3", "bio4", "bio5", "bio6", "bio7", "bio8", "bio9")
current <- setMinMax(current)
projection(current) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"
plot(current[[1]])
Projection layers
Projection layers can be a different geographic region or different time period.
env.files.p <- list.files(path = "data/climate_processing/Coastal_Plains_Layers/", pattern = ".asc", full.names = TRUE) #Always check for tiff or asc
proj <- stack(env.files.p)
names(proj) <- c("alt", "bio1", "bio10", "bio11", "bio12", "bio13", "bio14", "bio15", "bio16", "bio17", "bio18", "bio19", "bio2", "bio3", "bio4", "bio5", "bio6", "bio7", "bio8", "bio9")
proj <- setMinMax(proj)
projection(proj) <- "+proj=longlat +ellps=WGS84 +towgs84=0,0,0,0,0,0,0 +no_defs"
plot(proj[[1]])
MESS
For MESS, the more negative the score, the more likely you are to project into novel environments.
###### Generate data frame
ref <- as.data.frame(current)
head(ref)## alt bio1 bio10 bio11 bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19 bio2
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 87 188 264 104 1635 180 81 18 478 324 478 419 126
## 3 82 188 264 103 1627 179 81 18 476 323 476 417 126
## 4 87 187 264 103 1626 179 82 18 477 323 477 416 126
## 5 86 187 264 103 1625 179 82 18 478 323 478 415 127
## 6 78 187 263 103 1623 179 81 18 479 322 479 415 127
## bio3 bio4 bio5 bio6 bio7 bio8 bio9
## 1 NA NA NA NA NA NA NA
## 2 42 6294 326 31 295 264 194
## 3 42 6304 326 31 295 264 194
## 4 42 6287 326 31 295 264 193
## 5 43 6291 325 31 294 264 193
## 6 43 6282 325 31 294 263 193Run MESS
mess <- mess(x = proj, v = ref, full = FALSE)
plot(mess)
Mahalanobis distance
For mahalanobis distances, zero equals completely overlapping. Comparatively, the higher the distance, the more likely you are to project into an environment that falls outside the range of the training region, leading to unreliable projections.
Greater numbers means the variables are more dissimilar.
Generate data frames
Generate data frames containing the environmental data.
refdat <- as.data.frame(ref)
head(refdat)## alt bio1 bio10 bio11 bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19 bio2
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 87 188 264 104 1635 180 81 18 478 324 478 419 126
## 3 82 188 264 103 1627 179 81 18 476 323 476 417 126
## 4 87 187 264 103 1626 179 82 18 477 323 477 416 126
## 5 86 187 264 103 1625 179 82 18 478 323 478 415 127
## 6 78 187 263 103 1623 179 81 18 479 322 479 415 127
## bio3 bio4 bio5 bio6 bio7 bio8 bio9
## 1 NA NA NA NA NA NA NA
## 2 42 6294 326 31 295 264 194
## 3 42 6304 326 31 295 264 194
## 4 42 6287 326 31 295 264 193
## 5 43 6291 325 31 294 264 193
## 6 43 6282 325 31 294 263 193projdat <- as.data.frame(proj)
head(projdat)## alt bio1 bio10 bio11 bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19 bio2
## 1 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NA NA NA NA NA NA NA
## bio3 bio4 bio5 bio6 bio7 bio8 bio9
## 1 NA NA NA NA NA NA NA
## 2 NA NA NA NA NA NA NA
## 3 NA NA NA NA NA NA NA
## 4 NA NA NA NA NA NA NA
## 5 NA NA NA NA NA NA NA
## 6 NA NA NA NA NA NA NACalculate the average and covariance matrix of the variables in the reference set.
ref.av <- colMeans(refdat, na.rm=TRUE)
ref.cov <- var(refdat, na.rm=TRUE)Calculate the mahalanobis distance of each raster cell to the environmental center of the reference set for both the reference and the projection data set and calculate the ratio between the two.
mah.ref <- mahalanobis(x=refdat, center=ref.av, cov=ref.cov, tol=1e-20)
mah.pro <- mahalanobis(x=projdat, center=ref.av, cov=ref.cov, tol=1e-20)
mah.max <- max(mah.ref[is.finite(mah.ref)])
nt2 <- as.data.frame(mah.pro / mah.max)Create and plot the raster layer.
NT2 <- read.asciigrid(env.files.p[[1]])
NT2@data <- nt2
NT2rast <- raster(NT2)
plot(NT2rast, col=rainbow(100))
***
Ecological Niche Modeling
MUST HAVE RJAVA INSTALLED
We will not demo these scripts during the workshop due to system requirements of rJava.
Script by Anthony Melton and ML Gaynor.
This script is for generating and testing ENMs using ENMEval. Please see https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12261 for the paper describing ENMEval and https://cran.r-project.org/web/packages/ENMeval/vignettes/ENMeval-vignette.html#cb2 for the vignette.
Set up java memory
options(java.parameters = "- Xmx16g") # increase memory that can be usedLoad Packages
library(dplyr)
library(ENMeval)
library(rSDM)
library(dismo)
library(spThin)
library(readr)
library(rmaxent)
library(gtools)Load Datafiles
Species occurrence records
This is the file you made at the end of the Occurrence_Data_Cleaning.R script.
florida_points <- read.csv("data/cleaning_demo/MaxEntPointsInput_cleaned.csv")Subset occurrence records for each species
Asclepias_curtissii <- florida_points %>%
filter(species == "Asclepias_curtissii")
Asimina_obovata <- florida_points %>%
filter(species == "Asimina_obovata")
Pinus_palustris <- florida_points %>%
filter(species == "Pinus_palustris")Raster layers
These files were made in the ClimateProcesssing.R script.
# Raster stack
list <- list.files("data/climate_processing/PresentLayers/", full.names = T, recursive = FALSE)
list <- mixedsort(sort(list))
envtStack <- stack(list)Thin occurrence records
If there are still a lot of points after cleaning, you can further reduce to minimize biases in g-space.
The following code requires the thin_max.R script by Dan Warren available at http://enmtools.blogspot.com/.
nrow(Pinus_palustris)## [1] 51if (nrow(Pinus_palustris) > 50) {
source("functions/thin.max.R")
Pinus_palustris <- thin.max(Pinus_palustris, c("long", "lat"), 50) # trim down to N; check column names
}
nrow(Pinus_palustris)## [1] 50plot(x = Pinus_palustris$lat, y = Pinus_palustris$long, col = "black")
Designate background data
Randomly sample 10,000 background points from one background extent raster (only one per cell without replacement). Note: Since the raster has <10,000 pixels, you’ll get a warning and all pixels will be used for background. We will be sampling from the biome variable because it is missing some grid cells, and we are trying to avoid getting background points with NA.
bg <- randomPoints(envt.subset[[1]], n=10000)
bg <- as.data.frame(bg)Check the bg point distribution in g-space
plot(envt.subset[[1]], legend=FALSE)
plot(x = bg$x, y = bg$y, pch = 16, col='red')
Model generation
ENMeval will generate multiple models and test them per the specified test method. Two important variables here are the regularization multiplier value and the feature class (fm). FC will allow for different shapes in response curves (linear, hinge, quadratic, product, and threshold) can be used in the model and RM will influence how many parameters are included in the model.
modeval <- ENMevaluate(occ = Pinus_palustris[, c("long", "lat")],
env = envt.subset,
bg.coords = bg,
#categoricals = "Taxonomy",
algorithm = 'maxent.jar',
#RMvalues = c(0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4),
#fc = c("L", "H", "LQ", "LQH", "LQP", "LQPH", "LQPHT"),
RMvalues = c(1, 2.5, 5),
fc = c("L", "H", "LQH"),
method = "block",
aggregation.factor = c(2,2),
#overlap = TRUE,
clamp = TRUE,
rasterPreds = TRUE,
parallel = TRUE,
numCores = 4,
bin.output = TRUE,
progbar = TRUE)Evaluating the Models
AICc score
Get the “best” model based on AICc score.
aic.opt <- modeval@models[[which(modeval@results$delta.AICc==0)]]
aic.opt## class : MaxEnt
## variables: bio2 bio3 bio5 bio8 bio9 bio12 bio14 bio18
## output html file no longer existsaic.opt@lambdas## [1] "bio12, 0.0, 616.0, 1635.0"
## [2] "bio14, 0.0, 23.0, 93.0"
## [3] "bio18, 0.0, 236.0, 660.0"
## [4] "bio2, 1.1480830825248387, 59.0, 138.0"
## [5] "bio3, -1.3121161746720424, 36.0, 54.0"
## [6] "bio5, 0.0, 310.0, 337.0"
## [7] "bio8, 0.0, 257.0, 278.0"
## [8] "bio9, 0.0, 123.0, 229.0"
## [9] "linearPredictorNormalizer, 0.5513150681853487"
## [10] "densityNormalizer, 4864.835031469543"
## [11] "numBackgroundPoints, 7807"
## [12] "entropy, 8.911469747354095"Lambda
The Parse_lambdas.R script by John Baums can be found at https://github.com/johnbaums/rmaxent/blob/master/R/parse_lambdas.R. When features that have a lambda of > 0, these are the features that are included in the model.
source("functions/parse_lambdas.R")
parse_lambdas(aic.opt) ##
## Features with non-zero weights
##
## feature lambda min max type
## bio2 1.148 59 138 linear
## bio3 -1.312 36 54 linear
##
##
## Features with zero weights
##
## feature lambda min max type
## bio12 0 616 1635 linear
## bio14 0 23 93 linear
## bio18 0 236 660 linear
## bio5 0 310 337 linear
## bio8 0 257 278 linear
## bio9 0 123 229 linearCreate a table with the stats for all models
results <- modeval@results
kable(results) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| settings | features | rm | train.AUC | avg.test.AUC | var.test.AUC | avg.diff.AUC | var.diff.AUC | avg.test.orMTP | var.test.orMTP | avg.test.or10pct | var.test.or10pct | AICc | delta.AICc | w.AIC | parameters | AUC_bin.1 | AUC_bin.2 | AUC_bin.3 | AUC_bin.4 | diff.AUC_bin.1 | diff.AUC_bin.2 | diff.AUC_bin.3 | diff.AUC_bin.4 | test.or10pct_bin.1 | test.or10pct_bin.2 | test.or10pct_bin.3 | test.or10pct_bin.4 | test.orMTP_bin.1 | test.orMTP_bin.2 | test.orMTP_bin.3 | test.orMTP_bin.4 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| L_1 | L | 1.0 | 0.6427 | 0.5950680 | 0.0769192 | 0.0951063 | 0.0145192 | 0.0833333 | 0.0277778 | 0.1233974 | 0.0395402 | 900.9929 | 9.8534940 | 0.0028874 | 8 | 0.5516992 | 0.4137206 | 0.8532727 | 0.5615794 | 0.1089778 | 0.1941263 | 0 | 0.0773210 | 0.0769231 | 0.4166667 | 0 | 0 | 0.0000000 | 0.3333333 | 0 | 0 |
| H_1 | H | 1.0 | 0.7128 | 0.6340071 | 0.0881188 | 0.1274079 | 0.0330382 | 0.1233974 | 0.0395402 | 0.2019231 | 0.0605276 | 929.6835 | 38.5441091 | 0.0000000 | 18 | 0.5191248 | 0.4542398 | 0.9008484 | 0.6618153 | 0.2281117 | 0.2338202 | 0 | 0.0476998 | 0.3076923 | 0.5000000 | 0 | 0 | 0.0769231 | 0.4166667 | 0 | 0 |
| LQH_1 | LQH | 1.0 | 0.7058 | 0.6245624 | 0.0879470 | 0.1276296 | 0.0286817 | 0.1041667 | 0.0434028 | 0.1442308 | 0.0575690 | 946.7588 | 55.6194488 | 0.0000000 | 21 | 0.5452257 | 0.4382712 | 0.9005281 | 0.6142244 | 0.1830345 | 0.2539500 | 0 | 0.0735339 | 0.0769231 | 0.5000000 | 0 | 0 | 0.0000000 | 0.4166667 | 0 | 0 |
| L_2.5 | L | 2.5 | 0.6250 | 0.6162346 | 0.1108003 | 0.0983665 | 0.0297436 | 0.0625000 | 0.0156250 | 0.1410256 | 0.0282709 | 894.4267 | 3.2872847 | 0.0769727 | 4 | 0.4527741 | 0.4172217 | 0.8861131 | 0.7088297 | 0.2185759 | 0.1748903 | 0 | 0.0000000 | 0.2307692 | 0.3333333 | 0 | 0 | 0.0000000 | 0.2500000 | 0 | 0 |
| H_2.5 | H | 2.5 | 0.6296 | 0.6047659 | 0.0595591 | 0.0894792 | 0.0256199 | 0.0416667 | 0.0069444 | 0.0817308 | 0.0138992 | 899.1838 | 8.0444577 | 0.0071339 | 6 | 0.4715196 | 0.4592780 | 0.7728074 | 0.7154583 | 0.2116216 | 0.1462953 | 0 | 0.0000000 | 0.0769231 | 0.2500000 | 0 | 0 | 0.0000000 | 0.1666667 | 0 | 0 |
| LQH_2.5 | LQH | 2.5 | 0.6257 | 0.6278310 | 0.1056784 | 0.0860274 | 0.0243050 | 0.0817308 | 0.0138992 | 0.1410256 | 0.0282709 | 895.4963 | 4.3569047 | 0.0450891 | 5 | 0.4629622 | 0.4385594 | 0.8920545 | 0.7177480 | 0.2094973 | 0.1346121 | 0 | 0.0000000 | 0.2307692 | 0.3333333 | 0 | 0 | 0.0769231 | 0.2500000 | 0 | 0 |
| L_5 | L | 5.0 | 0.6221 | 0.6222853 | 0.0983180 | 0.0806673 | 0.0293248 | 0.0400641 | 0.0021470 | 0.1394231 | 0.0264731 | 891.1394 | 0.0000000 | 0.3982554 | 2 | 0.4305357 | 0.4683350 | 0.8688504 | 0.7214199 | 0.2421766 | 0.0804926 | 0 | 0.0000000 | 0.3076923 | 0.2500000 | 0 | 0 | 0.0769231 | 0.0833333 | 0 | 0 |
| H_5 | H | 5.0 | 0.6036 | 0.5674198 | 0.0217130 | 0.0529846 | 0.0252663 | 0.0000000 | 0.0000000 | 0.0000000 | 0.0000000 | 893.7779 | 2.6385224 | 0.1064667 | 2 | 0.4663615 | 0.5000000 | 0.6516588 | 0.6516588 | 0.2119382 | 0.0000000 | 0 | 0.0000000 | 0.0000000 | 0.0000000 | 0 | 0 | 0.0000000 | 0.0000000 | 0 | 0 |
| LQH_5 | LQH | 5.0 | 0.6225 | 0.6171537 | 0.1022417 | 0.0855095 | 0.0342282 | 0.0400641 | 0.0021470 | 0.1586538 | 0.0365816 | 891.3237 | 0.1843087 | 0.3631947 | 2 | 0.4145146 | 0.4683350 | 0.8682198 | 0.7175452 | 0.2615454 | 0.0804926 | 0 | 0.0000000 | 0.3846154 | 0.2500000 | 0 | 0 | 0.0769231 | 0.0833333 | 0 | 0 |
Visualize model statistics
Relative occurrence rate
plot(modeval@predictions[[which(modeval@results$delta.AICc==0)]], main="Relative occurrence rate")
Variable importance
(df <- var.importance(aic.opt))## variable percent.contribution permutation.importance
## 1 bio12 0.0000 0.0000
## 2 bio14 0.0000 0.0000
## 3 bio18 0.0000 0.0000
## 4 bio2 25.4036 45.1148
## 5 bio3 74.5964 54.8852
## 6 bio5 0.0000 0.0000
## 7 bio8 0.0000 0.0000
## 8 bio9 0.0000 0.0000barplot(df$permutation.importance, names.arg=df$variable, las=2, ylab="Permutation Importance")
delta.AICc
eval.plot(results)
Visualize Predicted Models
Plots all of the predictions by the different models
maps <- modeval@predictions
plot(maps)
Plots the predictions of the best model
plot(maps[[which(results$delta.AICc == 0)]], main = "Models with lowest AICc")
Response curves
Plots all of the response curves for the best model.
for (i in which(results$delta.AICc == 0)) {
response(modeval@models[[i]])
}
The “best” model
# Pick "best" model based on AICc
which(results$delta.AICc == 0)## [1] 7n <- which(results$delta.AICc == 0)
# Isolate the "best" model
modeval@predictions[[n]] # raw output; Check model name!## class : RasterLayer
## dimensions : 155, 182, 28210 (nrow, ncol, ncell)
## resolution : 0.04166667, 0.04166667 (x, y)
## extent : -87.625, -80.04167, 24.54167, 31 (xmin, xmax, ymin, ymax)
## crs : NA
## source : memory
## names : L_5
## values : 0.00006318226, 0.0002055568 (min, max)p <- predict(modeval@models[[n]], envt.subset)
plot(p, zlim = c(0,1)) # plots the predictions in a 0-1 scale; logistic output
Save output
R saved datset
save(modeval, file = "PineENM.rda")
# load(file = "FILENAME.rda")Raster
writeRaster(x = p, filename = "PineENM.asc", format = "ascii", NAFlag = "-9999", overwrite = T)Alternative: ENMTools
Load Packages
library(raster)
library(ENMTools)
library(ENMeval)
library(gtools)
library(dplyr)
library(RStoolbox)
library(hypervolume)
library(phytools)
library(dismo)
#library(alphanull)ENMTools
We are using ENMTools to generate ENM models using MAXENT. This package may cause you trouble, for troubleshooting, see set-up.
Correct data format to be used to generate models.
envt.subset <- setMinMax(envt.subset)
proj4string(envt.subset) <- crs("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")Correct species format to required ENMTools input.
Asclepias_curtissii_enm <- enmtools.species(species.name = "Asclepias_curtissii",
presence.points = Asclepias_curtissii[,3:2])
Asclepias_curtissii_enm$range <- background.raster.buffer(Asclepias_curtissii_enm$presence.points, 25000, mask = envt.subset)
Asclepias_curtissii_enm$background.points = background.points.buffer(points = Asclepias_curtissii_enm$presence.points, radius = 5000, n = 10000, mask = envt.subset[[1]])
##############################
Asimina_obovata_enm <- enmtools.species(species.name = "Asimina_obovata",
presence.points = Asimina_obovata[,3:2])
Asimina_obovata_enm$range <- background.raster.buffer(Asimina_obovata_enm$presence.points, 25000, mask = envt.subset)
Asimina_obovata_enm$background.points = background.points.buffer(points = Asimina_obovata_enm$presence.points, radius = 5000, n = 10000, mask = envt.subset[[1]])
#############################
Pinus_palustris_enm <- enmtools.species(species.name = "Pinus_palustris",
presence.points = Pinus_palustris[,3:2])
Pinus_palustris_enm$range <- background.raster.buffer(Pinus_palustris_enm$presence.points, 25000, mask = envt.subset)
Pinus_palustris_enm$background.points = background.points.buffer(points = Pinus_palustris_enm$presence.points, radius = 5000, n = 10000, mask = envt.subset[[1]])Run each model
Use MAXENT and generate models. These models will be used in the next tab. Here, the proportion of data to withhold randomly is set to 0.25. To match the GUI settings, add rts.reps = 5.
Asclepias_curtissii_enm.mx <- enmtools.maxent(species = Asclepias_curtissii_enm, env = envt.subset, test.prop = 0.25)
Asimina_obovata_enm.mx <- enmtools.maxent(species = Asimina_obovata_enm, env = envt.subset, test.prop = 0.25)
Pinus_palustris_enm.mx <- enmtools.maxent(species = Pinus_palustris_enm, env = envt.subset, test.prop = 0.25)Save models
save(Asclepias_curtissii_enm.mx, file = "data/enm_output/ENMTools/Asclepias_curtissii.rda")
save(Asimina_obovata_enm.mx, file = "data/enm_output/ENMTools/Asimina_obovata.rda" )
save(Pinus_palustris_enm.mx, file = "data/enm_output/ENMTools/Pinus_palustris.rda" )Post-ENMs
Script by Anthony Melton and ML Gaynor.
Load Packages
library(raster)
library(ENMTools)
library(ENMeval)
library(gtools)
library(dplyr)
library(RStoolbox)
library(hypervolume)
library(phytools)
library(dismo)
#library(alphanull)ENM models
You can also read the models you generated with the MaxEnt GUI into R.
Asclepias_curtissii_enm.mx.b <- raster("data/enm_output/Asclepias_curtissii/Asclepias_curtissii_avg.asc")
Asimina_obovata_enm.mx.b <- raster("data/enm_output/Asclepias_obovata/Asimina_obovata_avg.asc")
Pinus_palustris_enm.mx.b <- raster("data/enm_output/Pinus_palustris/Pinus_palustris_avg.asc")See Ecological_Niche_Modeling.R to generate models in R.
load(file = "data/enm_output/ENMTools/Asclepias_curtissii.rda")
load(file = "data/enm_output/ENMTools/Asimina_obovata.rda")
load(file = "data/enm_output/ENMTools/Pinus_palustris.rda")Bioclim layers
These steps are described in the previous script.
list <- list.files("data/climate_processing/PresentLayers/", full.names = T, recursive = FALSE)
list <- mixedsort(sort(list))
envtStack <- stack(list)
envt.subset<-subset(envtStack, c(3, 4, 6, 9, 10, 13, 15, 19))
envt.subset <- setMinMax(envt.subset)
proj4string(envt.subset) <- crs("+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0")ENM Breadth
Niche breadth is the breadth of suitable climatic factors for a species, ranging from 0 to 1. When breadth is closer to 1 the more generalist species with wider climatic tolerances. Values closer to 0 represent more specialized species. The raster.breadth command in ENMTools measures the smoothness of suitability scores across a projected landscape. The higher the score, the more of the available niche space a species occupies.
Rbased
Ac_breadth <- ENMTools::raster.breadth(x = Asclepias_curtissii_enm.mx$suitability)
Ac_breadth$B2## [1] 0.9884032Ao_breadth <- ENMTools::raster.breadth(x = Asimina_obovata_enm.mx$suitability)
Ao_breadth$B2## [1] 0.998853Pp_breadth <- ENMTools::raster.breadth(x= Pinus_palustris_enm.mx$suitability)
Pp_breadth$B2## [1] 0.9271576MaxEnt GUI
Ac_breadth.b <- ENMTools::raster.breadth(x = Asclepias_curtissii_enm.mx.b)
Ac_breadth.b$B2## [1] 0.7653468Ao_breadth.b <- ENMTools::raster.breadth(x = Asimina_obovata_enm.mx.b)
Ao_breadth.b$B2## [1] 0.9527736Pp_breadth.b <- ENMTools::raster.breadth(x = Pinus_palustris_enm.mx.b)
Pp_breadth.b$B2## [1] 0.8838691ENM overlap
Calculating niche overlap, Schoener’s D, with ENMEval - Schoener’s D ranges from 0 to 1, where zero represents no similarity between the projections and one represents completely identical projections.
Rbased
### Stack the enm models and make sure the stack is named.
enm_stack <- stack(Asclepias_curtissii_enm.mx$suitability, Asimina_obovata_enm.mx$suitability,Pinus_palustris_enm.mx$suitability)
names(enm_stack) <- c("Asclepias_curtissii", "Asimina_obovata", "Pinus_palustris")
### Calculate
calc.niche.overlap(enm_stack, stat = "D")##
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|=================================== | 50%
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|======================================================================| 100%## Asclepias_curtissii Asimina_obovata Pinus_palustris
## Asclepias_curtissii NA NA NA
## Asimina_obovata 0.9677212 NA NA
## Pinus_palustris 0.8795803 0.8852847 NAMaxEnt GUI
## MaxEnt GUI files
### Stack the enm models and make sure the stack is named.
enm_stack.b <- stack(Asclepias_curtissii_enm.mx.b, Asimina_obovata_enm.mx.b,Pinus_palustris_enm.mx.b)
names(enm_stack.b) <- c("Asclepias_curtissii", "Asimina_obovata", "Pinus_palustris")
### Calculate
calc.niche.overlap(enm_stack.b, stat = "D")##
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|=================================== | 50%
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|======================================================================| 100%## Asclepias_curtissii Asimina_obovata Pinus_palustris
## Asclepias_curtissii NA NA NA
## Asimina_obovata 0.8156201 NA NA
## Pinus_palustris 0.6427275 0.7844774 NAHypervolume
Niche space can be thought of as a multi-dimensional hypervolume. We’re using climatic data in this case, so we’re measuring the hypervolume of climatic niche space occupied by these species in FL.
The following command generates a binary predicted occurrence map. To run this command, a function must be generated. There are two functions in the Functions.R script. By sourcing the script, the functions will be generated and you can run them here!
source("functions/Functions_AEM.R") Load occurrence data
florida_points <- read.csv("data/cleaning_demo/MaxEntPointsInput_cleaned.csv")
Asclepias_curtissii.pts <- florida_points %>%
filter(species == "Asclepias_curtissii")
Asimina_obovata.pts <- florida_points %>%
filter(species == "Asimina_obovata")
Pinus_palustris.pts <- florida_points %>%
filter(species == "Pinus_palustris")Create the binary maps
We will generate a binary map with any cell that contains a suitability score greater than or equal to the lowest score with an occurrence point as a presence. There are other ways to set the threshold, including percentiles or training statistics.
Asimina.dist <- make.binary.map(model = Asimina_obovata_enm.mx.b, occ.dat = Asimina_obovata.pts)
Pinus.dist <- make.binary.map(model = Pinus_palustris_enm.mx.b, occ.dat = Pinus_palustris.pts)
Asclepias.dist <- make.binary.map(model = Asclepias_curtissii_enm.mx.b, occ.dat = Asclepias_curtissii.pts)
Calculate hypervolume
Next, let’s work on getting some data from the predicted distributions! Niche space can be thought of as a multi-dimensional hypervolume. We’re using climatic data in this case, so we’re measuring the hypervolume of climatic niche space occupied by these species in FL.
Asimina.hv <- get_hypervolume(binary_projection = Asimina.dist, envt = envt.subset)##
## Building tree...
## done.
## Ball query...
##
## done.
## Binding random points... done.
## Beginning volume calculation... done.Pinus.hv <- get_hypervolume(binary_projection = Pinus.dist, envt = envt.subset)##
## Building tree...
## done.
## Ball query...
##
## done.
## Binding random points... done.
## Beginning volume calculation... done.Asclepias.hv <- get_hypervolume(binary_projection = Asclepias.dist, envt = envt.subset)##
## Building tree...
## done.
## Ball query...
##
## done.
## Binding random points... done.
## Beginning volume calculation... done.Compare the hypervolumes
hv_set <- hypervolume_set(hv1 = Asimina.hv, hv2 = Pinus.hv, check.memory = F)## Choosing num.points.max=673639 (use a larger value for more accuracy.)
## Using minimum density of 1114.529403
## Retaining 111377 points in hv1 and 111584 points in hv2.
## Beginning ball queries...
##
## Building tree...
## done.
## Ball query...
##
## done.
##
## Building tree...
## done.
## Ball query...
##
## done.
## Finished ball queries.hypervolume_overlap_statistics(hv_set)## jaccard sorensen frac_unique_1 frac_unique_2
## 0.6414567 0.7815700 0.2177020 0.2191566plot(hv_set)
get_volume(hv_set)## untitled
## 99.93186
## untitled
## 100.11801
## Intersection of (untitled, untitled)
## 78.17649
## Union of (untitled, untitled)
## 121.87338
## Unique component of (untitled) relative to (untitled)
## 21.75537
## Unique component of (untitled) relative to (untitled)
## 21.94152Phylogenetic correlations
Let’s look at niche overlap over a tree!
Prepare data files
Load a tree file
tree <- read.newick(file = "data/AOC_Test_Demo/tree.tre")
tree <- ape::compute.brlen(phy = tree, method = "grafen")
plot(tree)
Add missing species
Liatris_chapmanii.pts <- read.csv("data/AOC_Test_Demo/clean_L_chapmaniiPointsSPOCC_EDITS.csv")[,2:3]Generate ENM models
Asclepias_curtissii <- enmtools.species(species.name = "Asclepias_curtissii",
presence.points = Asclepias_curtissii.pts[,3:2])
Asclepias_curtissii$range <- background.raster.buffer(Asclepias_curtissii$presence.points, 25000, mask = envt.subset)
Asclepias_curtissii$background.points = background.points.buffer(points = Asclepias_curtissii$presence.points, radius = 5000, n = 10000, mask = envt.subset[[1]])
##############################
Asimina_obovata <- enmtools.species(species.name = "Asimina_obovata",
presence.points = Asimina_obovata.pts[,3:2])
Asimina_obovata$range <- background.raster.buffer(Asimina_obovata$presence.points, 25000, mask = envt.subset)
Asimina_obovata$background.points = background.points.buffer(points = Asimina_obovata$presence.points, radius = 5000, n = 10000, mask = envt.subset[[1]])
#############################
Pinus_palustris <- enmtools.species(species.name = "Pinus_palustris",
presence.points = Pinus_palustris.pts[,3:2])
Pinus_palustris$range <- background.raster.buffer(Pinus_palustris$presence.points, 25000, mask = envt.subset)
Pinus_palustris$background.points = background.points.buffer(points = Pinus_palustris$presence.points, radius = 5000, n = 10000, mask = envt.subset[[1]])
#############################
Liatris_chapmanii <- enmtools.species(species.name = "Liatris_chapmanii",
presence.points = Liatris_chapmanii.pts)
Liatris_chapmanii$range <- background.raster.buffer(Liatris_chapmanii$presence.points, 25000, mask = envt.subset)
Liatris_chapmanii$background.points = background.points.buffer(points = Liatris_chapmanii$presence.points, radius = 5000, n = 10000, mask = envt.subset[[1]])Create “clade” object
clade=enmtools.clade(species = list(Asclepias_curtissii, Pinus_palustris, Asimina_obovata, Liatris_chapmanii), tree = tree)
check.clade(clade)##
##
## An enmtools.clade object with 4 species
##
## Species names:
## Asclepias_curtissii Liatris_chapmanii Asimina_obovata Pinus_palustris
##
## Tree:
##
## Phylogenetic tree with 4 tips and 3 internal nodes.
##
## Tip labels:
## [1] "Asclepias_curtissii" "Liatris_chapmanii" "Asimina_obovata"
## [4] "Pinus_palustris"
##
## Rooted; includes branch lengths.
##
##
## Data Summary:
## <table>
## <thead>
## <tr>
## <th style="text-align:left;"> </th>
## <th style="text-align:left;"> species.names </th>
## <th style="text-align:left;"> in.tree </th>
## <th style="text-align:left;"> presence </th>
## <th style="text-align:left;"> background </th>
## <th style="text-align:left;"> range </th>
## </tr>
## </thead>
## <tbody>
## <tr>
## <td style="text-align:left;"> Asclepias_curtissii </td>
## <td style="text-align:left;"> Asclepias_curtissii </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 21 </td>
## <td style="text-align:left;"> 92 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Liatris_chapmanii </td>
## <td style="text-align:left;"> Liatris_chapmanii </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 84 </td>
## <td style="text-align:left;"> 168 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Asimina_obovata </td>
## <td style="text-align:left;"> Asimina_obovata </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 17 </td>
## <td style="text-align:left;"> 69 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## <tr>
## <td style="text-align:left;"> Pinus_palustris </td>
## <td style="text-align:left;"> Pinus_palustris </td>
## <td style="text-align:left;"> TRUE </td>
## <td style="text-align:left;"> 51 </td>
## <td style="text-align:left;"> 217 </td>
## <td style="text-align:left;"> present </td>
## </tr>
## </tbody>
## </table>Correlation Tests
For range AOCs, an intercept >0.5 and negative slope are indicative of sympatric species, while an intercept of <0.5 and a positive slope are indicative on non-sympatric speciation. A low intercept and positive slope for niche overlap would indicate niche divergence.
Age-Range Correlation Test
range.aoc <- enmtools.aoc(clade = clade, nreps = 10, overlap.source = "range")
summary(range.aoc)##
##
## Age-Overlap Correlation test
##
## 10 replicates
##
## p values:
## (Intercept) empirical.df$age
## 0.1818182 0.1818182
## NULLAge-Overlap Correlation Test
glm.aoc <- enmtools.aoc(clade = clade, env = envt.subset, nreps = 10, overlap.source = "glm")##
##
## Drawing background from species background points.
##
## Adding environmental data to species Asclepias_curtissii
## Processing presence points...
## Processing background points...
##
##
## Drawing background from species background points.
##
##
##
## Drawing background from species background points.
##
## Adding environmental data to species Liatris_chapmanii
## Processing presence points...
## Processing background points...
##
##
## Drawing background from species background points.
##
##
##
## Drawing background from species background points.
##
## Adding environmental data to species Asimina_obovata
## Processing presence points...
## Processing background points...
##
##
## Drawing background from species background points.
##
##
##
## Drawing background from species background points.
##
## Adding environmental data to species Pinus_palustris
## Processing presence points...
## Processing background points...
##
##
## Drawing background from species background points.summary(glm.aoc)##
##
## Age-Overlap Correlation test
##
## 10 replicates
##
## p values:
## (Intercept) empirical.df$age
## 0.7272727 0.9090909
## NULLPhylogenetic diversity
Original script by J. Janzten. Modified by ML Gaynor.
Load Packages
library(raster)
library(ape)
library(phytools)
library(picante)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(rgdal)
library(rgeos)
library(sp)
library(spatialEco)Load the tree file
tree <- read.tree(file = "data/phylogenetic_diversity/tree_withBL.tre")
tree <- ape::compute.brlen(phy = tree, method = "grafen")
plot(tree)
Read ENM models
Asclepias_curtissii_enm.mx.b <- raster("data/enm_output/Asclepias_curtissii/Asclepias_curtissii_avg.asc")
Asimina_obovata_enm.mx.b <- raster("data/enm_output/Asclepias_obovata/Asimina_obovata_avg.asc")
Pinus_palustris_enm.mx.b <- raster("data/enm_output/Pinus_palustris/Pinus_palustris_avg.asc")
Liatris_chapmanii_enm.mx.b <- raster("data/phylogenetic_diversity/Liatris_chapmanii/Liatris_chapmanii_avg.asc")Reclassify and stack rasters
Reclassify
reclassify_raster <- function(OGraster){
## Reclassify the raster by the suitability score
OGraster[OGraster >= 0.25] <- 1
OGraster[OGraster < 0.25] <- 0
OGraster[is.na(OGraster)] <- 0
crs(OGraster) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
return(OGraster)
}
Asclepias_curtissii_enm.mx.b <- reclassify_raster(Asclepias_curtissii_enm.mx.b)
Asimina_obovata_enm.mx.b <- reclassify_raster(Asimina_obovata_enm.mx.b)
Pinus_palustris_enm.mx.b <- reclassify_raster(Pinus_palustris_enm.mx.b)
Liatris_chapmanii_enm.mx.b <- reclassify_raster(Liatris_chapmanii_enm.mx.b)Inspect reclassified rasters
par(mfrow=c(1,4))
plot(Asclepias_curtissii_enm.mx.b)
plot(Asimina_obovata_enm.mx.b)
plot(Pinus_palustris_enm.mx.b)
plot(Liatris_chapmanii_enm.mx.b)
Stack and convert to dataframe
# Stack rasters and create dataframe
rasterstack <- stack(Asclepias_curtissii_enm.mx.b, Asimina_obovata_enm.mx.b,
Pinus_palustris_enm.mx.b, Liatris_chapmanii_enm.mx.b)
## Rename
names(rasterstack) <- c("Asclepias_curtissii1", "Asimina_obovata1", "Pinus_palustris1", "Liatris_chapmanii1")
## Convert to dataframe
rasterstack_df <- as.data.frame(rasterstack, xy = TRUE)Extract ecoregions
Load Florida shapefile
Load the shapefile and correct the CRS (coordinate reference system), then crop to the area of interest.
## Load Florida shapefile
### Obtained at https://www.epa.gov/eco-research/ecoregion-download-files-state-region-4
shp <- ("data/phylogenetic_diversity/Florida_Raster/reg4_eco_l4/reg4_eco_l4.shp")
floridashape <- shapefile(shp)
## Correct CRS
newcrs <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
floridashape2 <- spTransform(floridashape, newcrs)
## Crop to florida
floridashape3 <- raster::crop(floridashape2, extent(-87.625, -80.04167, 24.54167, 31))Convert dataframe to points
df.sp <- rasterstack_df[,1:2]
coordinates(df.sp)<-~x+y
crs(df.sp) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"Extract points from shapefile
df.sp.info <- sp::over(df.sp, floridashape3)Bind dataframe with points and with ecosystems
rasterstack_df.info <- cbind(df.sp.info, rasterstack_df)
rasterstack_df.info2 <- rasterstack_df.info %>%
dplyr::group_by(L4_KEY) %>%
dplyr::summarize(Asclepias_curtissii = max(Asclepias_curtissii1),
Asimina_obovata = max(Asimina_obovata1),
Pinus_palustris = max(Pinus_palustris1),
Liatris_chapmanii = max(Liatris_chapmanii1))
rasterstack_df.info2df <- as.data.frame(rasterstack_df.info2, row.names = FALSE)
rasterstack_df.info4 <- rasterstack_df.info2df[1:20,]
rasterstack_df.info3 <- rasterstack_df.info4[,-1]
row.names(rasterstack_df.info3) <- NULL
row.names(rasterstack_df.info3) <- rasterstack_df.info4[,1]
kable(rasterstack_df.info3) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| Asclepias_curtissii | Asimina_obovata | Pinus_palustris | Liatris_chapmanii | |
|---|---|---|---|---|
| 65f Southern Pine Plains and Hills | 0 | 1 | 1 | 1 |
| 65g Dougherty Plain | 0 | 1 | 1 | 1 |
| 65h Tifton Upland | 0 | 1 | 1 | 1 |
| 65o Tallahasee Hills/Valdosta Limesink | 0 | 1 | 1 | 1 |
| 65p Southeastern Floodplains and Low Terraces | 0 | 1 | 1 | 1 |
| 75a Gulf Coast Flatwoods | 1 | 1 | 1 | 1 |
| 75b Southwestern Florida Flatwoods | 1 | 1 | 1 | 1 |
| 75c Central Florida Ridges and Uplands | 1 | 1 | 1 | 1 |
| 75d Eastern Florida Flatwoods | 1 | 1 | 1 | 1 |
| 75e Okefenokee Plains | 0 | 1 | 1 | 1 |
| 75f Sea Island Flatwoods | 1 | 1 | 1 | 1 |
| 75g Okefenokee Swamp | 0 | 1 | 1 | 1 |
| 75i Floodplains and Low Terraces | 0 | 1 | 1 | 1 |
| 75j Sea Islands/Coastal Marsh | 1 | 1 | 1 | 0 |
| 75k Gulf Barrier Islands and Coastal Marshes | 1 | 1 | 1 | 1 |
| 75l Big Bend Coastal Marsh | 1 | 1 | 1 | 1 |
| 76a Everglades | 1 | 1 | 1 | 1 |
| 76b Big Cypress | 1 | 1 | 1 | 1 |
| 76c Miami Ridge/Atlantic Coastal Strip | 1 | 1 | 1 | 1 |
| 76d Southern Coast and Islands | 1 | 1 | 1 | 1 |
Phylogenetic Diversity Calculations
Match tree and dataframe
matched <- match.phylo.comm(tree, rasterstack_df.info3)# Make object for tree including matching taxa only (non-matching taxa will be pruned out)
matchtree <- matched$phy
# Make object for dataset including matching taxa only (non-matching taxa removed)
matchcomm <- matched$comm Calculate phylogenetic distance matrix for use in MPD and MNTD calculations
MPD is mean pairwise distance, or mean phylogenetic distance among all pairs of species within a community. MNTD is mean nearest taxon distance, or the mean distance between each species within a community and its closest relative.
phydist <- cophenetic(matched$phy)Calculate indices
Null model options include taxa.labels (shuffle tips of phylogeny) amongothers Use ses.pd, ses.mpd and ses.mntd for more info on alternative null models and other parameters and output format. Number of runs - only using 99 runs (comparing to null model) due to time constraints. Ideally, you should have 1,000 - 5,000 runs.
PD
PD calculates Faith’s PD and standard effect size of PD. PD is phylogenetic distance.
pd_result <-ses.pd(matchcomm, matchtree, null.model="taxa.labels", runs=99)MPD
Calculates mpd and ses mpd - equivalent to -NRI. NRI is net-relatedness index, standardizes MPD or mean pairwise distance. ses stands for standardized effect size.
If we had abundance data, we could include that for mpd and mntd. The default is abundance.weighted = FALSE.
mpd_result <- ses.mpd(matchcomm, phydist, null.model="taxa.labels", abundance.weighted = FALSE, runs=99)MNTD
Calculates mntd and ses mntd - equivalent to -NTI. NTI or nearest taxon index, standardizes MNTD mean nearest taxon distance. ses stands for standardized effect size.
mntd_result <- ses.mntd(matchcomm, phydist, null.model="taxa.labels", runs=99)Look at results
Positive ses values (obs.z values) and p values > 0.95 indicate overdispersion (greater than expected).
Negative ses values (obs.z values) and p values < 0.05 indicate clustering (less than expected).
Values not significantly different from zero indicate taxa in community randomly distributed across tree.
kable(pd_result) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| ntaxa | pd.obs | pd.rand.mean | pd.rand.sd | pd.obs.rank | pd.obs.z | pd.obs.p | runs | |
|---|---|---|---|---|---|---|---|---|
| 65f Southern Pine Plains and Hills | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 65g Dougherty Plain | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 65h Tifton Upland | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 65o Tallahasee Hills/Valdosta Limesink | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 65p Southeastern Floodplains and Low Terraces | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 75a Gulf Coast Flatwoods | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75b Southwestern Florida Flatwoods | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75c Central Florida Ridges and Uplands | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75d Eastern Florida Flatwoods | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75e Okefenokee Plains | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 75f Sea Island Flatwoods | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75g Okefenokee Swamp | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 75i Floodplains and Low Terraces | 3 | 2.666667 | 2.447811 | 0.2658219 | 73.0 | 0.8233154 | 0.730 | 99 |
| 75j Sea Islands/Coastal Marsh | 3 | 2.666667 | 2.393939 | 0.2910752 | 76.0 | 0.9369648 | 0.760 | 99 |
| 75k Gulf Barrier Islands and Coastal Marshes | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75l Big Bend Coastal Marsh | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76a Everglades | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76b Big Cypress | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76c Miami Ridge/Atlantic Coastal Strip | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76d Southern Coast and Islands | 4 | 3.000000 | 3.000000 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
kable(mpd_result) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| ntaxa | mpd.obs | mpd.rand.mean | mpd.rand.sd | mpd.obs.rank | mpd.obs.z | mpd.obs.p | runs | |
|---|---|---|---|---|---|---|---|---|
| 65f Southern Pine Plains and Hills | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 65g Dougherty Plain | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 65h Tifton Upland | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 65o Tallahasee Hills/Valdosta Limesink | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 65p Southeastern Floodplains and Low Terraces | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 75a Gulf Coast Flatwoods | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75b Southwestern Florida Flatwoods | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75c Central Florida Ridges and Uplands | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75d Eastern Florida Flatwoods | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75e Okefenokee Plains | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 75f Sea Island Flatwoods | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75g Okefenokee Swamp | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 75i Floodplains and Low Terraces | 3 | 1.777778 | 1.533109 | 0.2758344 | 78.0 | 0.8870138 | 0.780 | 99 |
| 75j Sea Islands/Coastal Marsh | 3 | 1.777778 | 1.616162 | 0.2350454 | 71.5 | 0.6875955 | 0.715 | 99 |
| 75k Gulf Barrier Islands and Coastal Marshes | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75l Big Bend Coastal Marsh | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76a Everglades | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76b Big Cypress | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76c Miami Ridge/Atlantic Coastal Strip | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76d Southern Coast and Islands | 4 | 1.555556 | 1.555556 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
kable(mntd_result) %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", font_size = 10)) %>%
scroll_box(width = "100%", height = "200px")| ntaxa | mntd.obs | mntd.rand.mean | mntd.rand.sd | mntd.obs.rank | mntd.obs.z | mntd.obs.p | runs | |
|---|---|---|---|---|---|---|---|---|
| 65f Southern Pine Plains and Hills | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 65g Dougherty Plain | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 65h Tifton Upland | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 65o Tallahasee Hills/Valdosta Limesink | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 65p Southeastern Floodplains and Low Terraces | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 75a Gulf Coast Flatwoods | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75b Southwestern Florida Flatwoods | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75c Central Florida Ridges and Uplands | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75d Eastern Florida Flatwoods | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75e Okefenokee Plains | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 75f Sea Island Flatwoods | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75g Okefenokee Swamp | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 75i Floodplains and Low Terraces | 3 | 1.555556 | 1.299663 | 0.2863549 | 73.5 | 0.8936193 | 0.735 | 99 |
| 75j Sea Islands/Coastal Marsh | 3 | 1.555556 | 1.292929 | 0.2881093 | 74.0 | 0.9115510 | 0.740 | 99 |
| 75k Gulf Barrier Islands and Coastal Marshes | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 75l Big Bend Coastal Marsh | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76a Everglades | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76b Big Cypress | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76c Miami Ridge/Atlantic Coastal Strip | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
| 76d Southern Coast and Islands | 4 | 1.166667 | 1.166667 | 0.0000000 | 50.5 | NaN | 0.505 | 99 |
Plot PD results by community
ggplot()+
geom_point(data = pd_result, aes(x = rownames(pd_result), y = pd.obs))+
ggtitle("PD values for FL by ecoregion")+
xlab("Community")+
ylab("PD")+
theme(text = element_text(size=10), axis.text.x = element_text(angle = 90, hjust = 1))
Plot MPD results by community
ggplot()+
geom_point(data = mpd_result, aes(x = rownames(mpd_result), y = mpd.obs))+
ggtitle("MPD values for FL by ecoregion")+
xlab("Community")+
ylab("MPD")+
theme(text = element_text(size=10), axis.text.x = element_text(angle = 90, hjust = 1))
Plot MNTD results by community
ggplot()+
geom_point(data = mntd_result, aes(x = rownames(mntd_result), y = mntd.obs))+
ggtitle("MNTD values for FL by ecoregion")+
xlab("Community")+
ylab("MNTD")+
theme(text = element_text(size=10), axis.text.x = element_text(angle = 90, hjust = 1))